{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T02:04:39.888174Z",
     "start_time": "2020-09-14T02:04:04.774909Z"
    }
   },
   "outputs": [],
   "source": [
    "# 引入常用库\n",
    "import talib  # 技术分析\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "\n",
    "import alphalens as al# 因子分析\n",
    "\n",
    "import pickle\n",
    "from tqdm import *\n",
    "import itertools  # 迭代器工具\n",
    "import datetime as dt\n",
    "from dateutil.parser import parser\n",
    "\n",
    "# 画图\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdate\n",
    "from matplotlib.pyplot import MultipleLocator\n",
    "import seaborn as sns\n",
    "\n",
    "# 设置字体 用来正常显示中文标签\n",
    "mpl.rcParams['font.sans-serif'] = ['serif']\n",
    "mpl.rcParams['font.family'] = 'serif'\n",
    "# 用来正常显示负号\n",
    "mpl.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 图表主题\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "# from jqdata import *\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据获取\n",
    "\n",
    "分笔数据来源优矿股票池为000002，399107的成分股在2020-05-06的截面成分股数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T02:04:46.224152Z",
     "start_time": "2020-09-14T02:04:39.889055Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py:580: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  mask |= (ar1 == a)\n"
     ]
    }
   ],
   "source": [
    "dealAmount = pd.read_csv(\n",
    "        'data2013_2020.csv', index_col=0, parse_dates=['tradeDate'])\n",
    "\n",
    "dealAmount = pd.pivot_table(dealAmount,index='tradeDate',columns='secID',values='dealAmount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T02:04:46.228113Z",
     "start_time": "2020-09-14T02:04:46.225121Z"
    }
   },
   "outputs": [],
   "source": [
    "start = min(dealAmount.index) # 起始日期\n",
    "end = max(dealAmount.index) # 结束日期\n",
    "codes = dealAmount.columns.tolist() # 股票代码\n",
    "\n",
    "# 数据获取\n",
    "daily_price = get_price(codes,start,end,fields=['money','close','pre_close'],panel=False)\n",
    "\n",
    "# 结构化\n",
    "money_df = pd.pivot_table(daily_price,index='time',columns='code',values='money')\n",
    "close_df = pd.pivot_table(daily_price,index='time',columns='code',values='close')\n",
    "pre_close = pd.pivot_table(daily_price,index='time',columns='code',values='pre_close')\n",
    "\n",
    "# 数据储存\n",
    "money_df.to_csv('money_df.csv')\n",
    "close_df.to_csv('close_df.csv')\n",
    "pre_close.to_csv('pre_close.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T02:04:53.393004Z",
     "start_time": "2020-09-14T02:04:46.230107Z"
    }
   },
   "outputs": [],
   "source": [
    "# 数据读取\n",
    "money_df = pd.read_csv('money_df.csv',index_col=0,parse_dates=[0])\n",
    "close_df = pd.read_csv('close_df.csv',index_col=0,parse_dates=[0])\n",
    "pre_close = pd.read_csv('pre_close.csv',index_col=0,parse_dates=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T02:04:53.914567Z",
     "start_time": "2020-09-14T02:04:53.393960Z"
    }
   },
   "outputs": [],
   "source": [
    "amt_df = money_df / dealAmount # 计算平均单笔成交金额\n",
    "ret_df = close_df / pre_close - 1 # 当日涨跌幅"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 因子构造"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "传统动量/反转因子(如常见的Ret20因子)，长期累计收益非常显著，短期却常常出现回撤。为了解决这种情况这里提出一种新的改进方案**\"W式切割\"**。比如咖啡成分李有苦与甜的对冲,那么在那么过去 20 日的涨跌幅里，为何不能分解出反转与动量的成分？这一问便直接引出了“反转因子的切割问题”。其构建方式如下:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|因子构造|\n",
    "|--|\n",
    "|步骤1:对选取股票S,回溯取其20日的数据;|\n",
    "|步骤2:计算股票S每日的**平均单笔成交金额**(成交金额/成交笔数);|\n",
    "|步骤3:单笔成交金额高的10个交易日,涨跌幅加总,记作M_high;|\n",
    "|步骤4:单笔成交金额低的10个交易日,涨跌幅加总,记作M_low;|\n",
    "|步骤5:理想反转因子M=M_high-M_low;|\n",
    "|步骤6:对所有股票进行以上操作计算各自的**理想反转因子M**|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-10T15:21:23.897742Z",
     "start_time": "2020-09-10T15:21:23.884748Z"
    }
   },
   "outputs": [],
   "source": [
    "def cala_w_factor(df1:pd.DataFrame,df2:pd.DataFrame,win_size:int)->pd.Series:\n",
    "    '''\n",
    "    df1,df2:数据需要对齐 以df1为基准对齐\n",
    "    df1,\n",
    "    '''\n",
    "    \n",
    "    # 数据对齐\n",
    "    df1,df2 = df1.align(df2,join='right')\n",
    "    \n",
    "    # rolling\n",
    "    iidx = np.arange(len(df1))\n",
    "    shape = (iidx.size - win_size + 1, win_size)\n",
    "    strides = (iidx.strides[0], iidx.strides[0])\n",
    "    res = np.lib.stride_tricks.as_strided(\n",
    "        iidx, shape=shape, strides=strides, writeable=True)\n",
    "    \n",
    "    # 因子计算\n",
    "    def _cal_m(df1:pd.DataFrame,df2:pd.DataFrame,res:list)->pd.Series:\n",
    "    \n",
    "        rank_df = df1.iloc[res].rank()\n",
    "        cond = (rank_df >= 11)\n",
    "        m_high = cond * df2.iloc[res]\n",
    "        m_low = ~cond * df2.iloc[res]\n",
    "\n",
    "        m_ser = m_high.sum() - m_low.sum()\n",
    "        m_ser.name = rank_df.index[-1]\n",
    "        \n",
    "        return m_ser\n",
    "    \n",
    "    return pd.concat([_cal_m(df1,df2,i) for i in res],axis=1).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-10T15:23:16.425119Z",
     "start_time": "2020-09-10T15:21:27.540807Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>603989.XSHG</th>\n",
       "      <th>603990.XSHG</th>\n",
       "      <th>603991.XSHG</th>\n",
       "      <th>603992.XSHG</th>\n",
       "      <th>603993.XSHG</th>\n",
       "      <th>603995.XSHG</th>\n",
       "      <th>603996.XSHG</th>\n",
       "      <th>603997.XSHG</th>\n",
       "      <th>603998.XSHG</th>\n",
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       "      <th>2013-01-31</th>\n",
       "      <td>0.239943</td>\n",
       "      <td>-0.181734</td>\n",
       "      <td>0.196284</td>\n",
       "      <td>0.208452</td>\n",
       "      <td>0.006382</td>\n",
       "      <td>0.498687</td>\n",
       "      <td>0.080432</td>\n",
       "      <td>0.264600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.058254</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.091353</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2013-02-01</th>\n",
       "      <td>0.217747</td>\n",
       "      <td>-0.177275</td>\n",
       "      <td>0.206838</td>\n",
       "      <td>0.208169</td>\n",
       "      <td>0.108105</td>\n",
       "      <td>0.566461</td>\n",
       "      <td>0.079361</td>\n",
       "      <td>0.263883</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.036524</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.132398</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2013-02-04</th>\n",
       "      <td>0.180769</td>\n",
       "      <td>-0.058559</td>\n",
       "      <td>0.242359</td>\n",
       "      <td>0.201676</td>\n",
       "      <td>-0.025814</td>\n",
       "      <td>0.581337</td>\n",
       "      <td>0.064319</td>\n",
       "      <td>0.283855</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.039607</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.140118</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-05</th>\n",
       "      <td>0.174962</td>\n",
       "      <td>-0.103514</td>\n",
       "      <td>0.199726</td>\n",
       "      <td>0.217641</td>\n",
       "      <td>-0.009729</td>\n",
       "      <td>0.542366</td>\n",
       "      <td>0.147407</td>\n",
       "      <td>0.272058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.172056</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2013-02-06</th>\n",
       "      <td>0.161158</td>\n",
       "      <td>-0.046942</td>\n",
       "      <td>0.167811</td>\n",
       "      <td>0.201818</td>\n",
       "      <td>-0.024767</td>\n",
       "      <td>0.578032</td>\n",
       "      <td>0.153769</td>\n",
       "      <td>0.235485</td>\n",
       "      <td>0.0</td>\n",
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       "<p>5 rows × 3704 columns</p>\n",
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      ],
      "text/plain": [
       "            000001.XSHE  000002.XSHE  000004.XSHE  000005.XSHE  000006.XSHE  \\\n",
       "2013-01-31     0.239943    -0.181734     0.196284     0.208452     0.006382   \n",
       "2013-02-01     0.217747    -0.177275     0.206838     0.208169     0.108105   \n",
       "2013-02-04     0.180769    -0.058559     0.242359     0.201676    -0.025814   \n",
       "2013-02-05     0.174962    -0.103514     0.199726     0.217641    -0.009729   \n",
       "2013-02-06     0.161158    -0.046942     0.167811     0.201818    -0.024767   \n",
       "\n",
       "            000007.XSHE  000008.XSHE  000009.XSHE  000010.XSHE  000011.XSHE  \\\n",
       "2013-01-31     0.498687     0.080432     0.264600          0.0     0.058254   \n",
       "2013-02-01     0.566461     0.079361     0.263883          0.0     0.036524   \n",
       "2013-02-04     0.581337     0.064319     0.283855          0.0     0.039607   \n",
       "2013-02-05     0.542366     0.147407     0.272058          0.0     0.100535   \n",
       "2013-02-06     0.578032     0.153769     0.235485          0.0     0.093121   \n",
       "\n",
       "            ...  603989.XSHG  603990.XSHG  603991.XSHG  603992.XSHG  \\\n",
       "2013-01-31  ...          0.0          0.0          0.0          0.0   \n",
       "2013-02-01  ...          0.0          0.0          0.0          0.0   \n",
       "2013-02-04  ...          0.0          0.0          0.0          0.0   \n",
       "2013-02-05  ...          0.0          0.0          0.0          0.0   \n",
       "2013-02-06  ...          0.0          0.0          0.0          0.0   \n",
       "\n",
       "            603993.XSHG  603995.XSHG  603996.XSHG  603997.XSHG  603998.XSHG  \\\n",
       "2013-01-31     0.091353          0.0          0.0          0.0          0.0   \n",
       "2013-02-01     0.132398          0.0          0.0          0.0          0.0   \n",
       "2013-02-04     0.140118          0.0          0.0          0.0          0.0   \n",
       "2013-02-05     0.172056          0.0          0.0          0.0          0.0   \n",
       "2013-02-06     0.146638          0.0          0.0          0.0          0.0   \n",
       "\n",
       "            603999.XSHG  \n",
       "2013-01-31          0.0  \n",
       "2013-02-01          0.0  \n",
       "2013-02-04          0.0  \n",
       "2013-02-05          0.0  \n",
       "2013-02-06          0.0  \n",
       "\n",
       "[5 rows x 3704 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M_factor = cala_w_factor(amt_df,ret_df,20)\n",
    "#M_factor.to_csv('Data/M_factor.csv')\n",
    "\n",
    "M_factor.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 因子分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "讲W反转因子按升序分为5组,调仓周期分别为1,5,10日"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-10T15:24:22.489699Z",
     "start_time": "2020-09-10T15:24:00.165190Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 19.5% entries from factor data: 19.5% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    }
   ],
   "source": [
    "#M_factor = pd.read_csv('Data\\M_factor.csv',index_col=0,parse_dates=[0])\n",
    "# price滞后一期避免未来函数\n",
    "M_factor_data = al.utils.get_clean_factor_and_forward_returns(M_factor.stack(),\n",
    "                                                              close_df.shift(-1),\n",
    "                                                              quantiles=5,\n",
    "                                                              periods=(1, 5, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到因子有较为不错的单调性,第一组有着较强的正向收益。第五组有较强的反向收益,反转能力较好。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-10T15:25:20.762312Z",
     "start_time": "2020-09-10T15:25:04.995380Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Mean Period Wise Return By Factor Quantile'}, ylabel='Mean Return (bps)'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看因子分组收益情况\n",
    "mean_quant_ret, std_quantile = al.performance.mean_return_by_quantile(\n",
    "    M_factor_data)\n",
    "\n",
    "mean_quant_rateret = mean_quant_ret.apply(\n",
    "    al.utils.rate_of_return, axis=0,base_period = '1D'\n",
    ")\n",
    "al.plotting.plot_quantile_returns_bar(mean_quant_rateret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-10T15:25:55.908748Z",
     "start_time": "2020-09-10T15:25:33.449358Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information Analysis\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1D</th>\n",
       "      <th>5D</th>\n",
       "      <th>10D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IC Mean</th>\n",
       "      <td>-0.006</td>\n",
       "      <td>-0.014</td>\n",
       "      <td>-0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IC Std.</th>\n",
       "      <td>0.133</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Risk-Adjusted IC</th>\n",
       "      <td>-0.045</td>\n",
       "      <td>-0.107</td>\n",
       "      <td>-0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t-stat(IC)</th>\n",
       "      <td>-1.858</td>\n",
       "      <td>-4.432</td>\n",
       "      <td>-4.891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value(IC)</th>\n",
       "      <td>0.063</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IC Skew</th>\n",
       "      <td>0.647</td>\n",
       "      <td>1.140</td>\n",
       "      <td>1.243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IC Kurtosis</th>\n",
       "      <td>1.744</td>\n",
       "      <td>2.438</td>\n",
       "      <td>2.360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1D     5D    10D\n",
       "IC Mean          -0.006 -0.014 -0.016\n",
       "IC Std.           0.133  0.133  0.138\n",
       "Risk-Adjusted IC -0.045 -0.107 -0.118\n",
       "t-stat(IC)       -1.858 -4.432 -4.891\n",
       "p-value(IC)       0.063  0.000  0.000\n",
       "IC Skew           0.647  1.140  1.243\n",
       "IC Kurtosis       1.744  2.438  2.360"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看IC\n",
    "ic = al.performance.factor_information_coefficient(M_factor_data)\n",
    "al.plotting.plot_information_table(ic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-10T15:33:01.841719Z",
     "start_time": "2020-09-10T15:32:24.545061Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1D</th>\n",
       "      <th>5D</th>\n",
       "      <th>10D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean Factor Rank Autocorrelation</th>\n",
       "      <td>0.916</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     1D     5D    10D\n",
       "Mean Factor Rank Autocorrelation  0.916  0.647  0.398"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看RANK IC\n",
    "periods = al.utils.get_forward_returns_columns(M_factor_data.columns)\n",
    "# jq [i for i in periods if i not in ['factor', 'factor_quantile']]\n",
    "periods = list(map(lambda p: pd.Timedelta(p).days, periods))\n",
    "\n",
    "autocorrelation = pd.concat(\n",
    "    [\n",
    "        al.performance.factor_rank_autocorrelation(M_factor_data, period)\n",
    "        for period in periods\n",
    "    ],\n",
    "    axis=1,\n",
    ")\n",
    "\n",
    "auto_corr = pd.DataFrame()\n",
    "for period, p_data in autocorrelation.iteritems():\n",
    "    auto_corr.loc[\"Mean Factor Rank Autocorrelation\",\n",
    "                  \"{}D\".format(period)] = p_data.mean()\n",
    "al.utils.print_table(auto_corr.apply(lambda x: x.round(3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-10T15:35:40.264690Z",
     "start_time": "2020-09-10T15:35:20.730492Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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87gnyLl1FrVGzZ+kaBv32MQouZ9CofRs2vTcf/4gwohJ6UlRgQEFh16KVdZKp95Sx2EtKCGwUzob/fFSlbcQzT5F76QpqjYadS1Yx7LePk38lg8j2bfnmf59iztfTe8pYLEYT/hFhbP54QZ1kips8htKSEnSNwtj6zqdV2hKffgz95QxcNGoOLF0LwOVDxxztvmEhtE7oSVlpKWFRrdj4l7frJFPS1AmU2EoIjowg+d/vVWmb9OyvyEq/gkajYfOS5fQaNRQVKtrFx/HdomXoc3J56Je/IO3Icbx8tHy3cGmdZLof9xPc2b4C6D5kIIacPABULi4c372XHiMG11keqN0xoTBfT0iLpnjW8XHqmtrsr7rwQJxpu7q6Et+jB8u+WoLRaCQmLtbRFte1K3qDnmVfLaFHr55oNBqSBg9m0ZcLOHLoEJOmTqWsrIwrly/XaSa1q4a23WLZsXwtRSYzLbt0crS1iu1MocHIjuVrieretfzAv+4bikxmXN3dyLp4CYDcyxmcPXSUS6lp902mmIEJHNy8vU7yAKg1Glp2iyFlxXqsJjNNozs42prFdMJiMJGyYj2tusfiolGza8lq9q7cQM6FdJp0bIc+M5sj324lIDKCC4eO12muFt26sG/FeiwmM00q5Woa04kig5F9K9bTsiLXniWr2bdyAzkXLtG4YzsKrmSw5YMv2bloBe0SetVZplbdYtizfB0Wk5lmXa6fhTWvyLRn+Tpax5dn2rlkFXtWrC/fV53aE9G2Fc1jOqFxdyPzzLk6yeSi0dC0WzSHVmzAajLTKLq9oy0ypiNWo4lDKzbQLD4GF40a/eWMKtsbM7M5+d02dJHhXD5cN+OndtXQLj6ObctXU2Qy0brSPG8TG43ZYGTb8tW071E+z3eu/ZoikwlXdzcyL6bTKroT2Zev8OOaDXQfmoTG1bXWme7H/QR3vq+aRLUmPTWNMqUMAKWsjNyrGbd6+LvLVMtjAkDHgX05/v2OOs0Ftd9fdcHpRdtkMrFy5Ur27t2LoigsWLCAefPmcfXq1Vtus2nTJubMmcOcOXNu+/iTpkzh1b/+BaVMASA/L5+IiEaO9rCICPLz8gGwFFnQ+fvz5htvMOORR2jRsiWBQUG1/A2r6zthNDNeehGlrHygzAUFBISHOtoDwkIxFxQAYLNY8Nb5AXBk6w5O7NpL54TeACz9x9v8uHIdgx+dhlqjrvdMzTq15/zRE4BSqyzXxI8fydiXfl8pkx5dpUy6sBDMBXpHJi8/P4zZubio1fhHhHF6514ACvP1fP32h3QZPrBOcnUbP4IxLz3ryFVYYLghVzCFBYaKXNYbcoVyZudeCgsMlNrttO7VjT1L19Q6U4/xo5jw8nMoFU9+c74e/xv3Vb7ekcnbzw/DtUzhYaT+mEJoi6YUFxaRsnID8WOHo3Fzq1WmLuOHM+xPzzj2U1G+Ab9KmXzDginKv76fPP18b/o4Rfl6tvz7YzoMG1CrPACJE8bw6MtzKKvIZMovIDA8zNEeEBaKKb98nhcXWdDqdAAc3LqDY7tSiEnow6HtO1CpVPR9aASKotT6ytL9uJ/g7vZVTEJfDm//sU76v5m6OCY07hjFpWOnUJS6OU5dc7/sL6cX7ffffx+DwcDmzZv56KOPcHNzIzY2lq+++uqW2yQlJTF37lzmzp1728df+tVXvPp/f0LlogIgIDCAq1evONozr14lIDAAAE8vT/QFBWRczeDL+fM5eeIEZ9Pq5iy2sh+S17Dgz2+gcinfvVp/f/Izshzt+ZlZaP39AXDz9KRQb6BD7+4AGHLz0IUE4R8ajJunJwClJSVo3NzrPVPjqNaENI6kbXwcUd27EhQZUatMKcvXsfLP8ypl0qGvlEmfmY3WX+fIVGQwoNZo6DN9PJve/xxtoD/NYjrh6umBoii4uNTNdN67fD2r//yWI5e3v98NuXLw9veryOXhyNVr+ng2v/8F2sDy/di0S0c0bq5cPnaq1pl2L19L8utvolJV7KsAHQU37qsAnSNTYUWmfjMm8M378/EJCsCUm4+1sAiAkmIbrh61K9qHlm9g41/eduwnrwA/DJUyGTNz8Aq4vp8sBmO1x4iM6YirpwcoiuNxamNr8mo+e32uYy74BPiTl5HpaM/PzMInoHx83L08Mev1dOrdAwB9Ti66kGBUqNixegM/rFpPocHoOBDfrftxP8Gd76uy0lLcvTwZMHkc/iHBxA9NqpMcldXFMSEiqjWBkRG07BZDq/hYAiLD6yTb/bK/nH5Pu3nz5kyYMAGAefPm8eSTTwKQn59fZ32UlJSQsns3E6dMxtfXl4P7DzB0+HCsVgtbv99C4oABTJwymd07d2G325k0dQr5eXkEBgXx4f/K70mMGD2akNBQusbHsy8lpdaZSkvspO49QJ/xo/Dy0XL20FHiBg/AZrVydPtOOvXrTZ/xozi1Zx+l9lK6DOiHX3AQzTq2Y/0H8/H286X/1PGkn0zl4vFUiouK6j2TIScXrc6PZp3ao5SVUWQy1z6T3c7ZvQeJHzcCDx8tFw8fp9PgREosVk79sJuofj2IHzeCtD0HKLOXMuPN17DbbAxv+iSZaefJOnuBgU8+jDE7l9Qfaz9ulXOd23uIruNG4OmjJf3wcToOTqDEUkxqRa6u40ZwtiLXtIpcQ5s+SVbaeS4dPcmoP/6Wy8dS6TH5IT6b/WKdZErbe5Du40fi6aPlwqFjRA9OpMRazIntu2if0Ivu40dyJqU806x5f6bEZmPUs5FknDnPj1+tpHNSAvEPDSfr7AUsxtqPX5ndzsW9h+kybjgePlquHD5B1KB+2K3FpP2wh1Z9u9Nl3HAupBykzF5KQLNIwtq1RuPmRtapNNSuGno/MR1zdi5nf9xb6zxQPs9PpuwnYfwYvHx8OHPoKPFDBlJssXJ4+490SehNwvgxnNhdPs/jBiagCwmiRYd2rP7gM7x8tDzy0oucTNnPj2s31skZ2/24n+DO9pWpQM/St/5Lx17dAYVCQ/nVgR7DB+MfEkTbuBhS9x+sfaZaHhN2Ll6Jl86Pxp3aoZSV1ck8d2Srg/1VWyqlrq8h3GDx4sVMnToVgLS0NFq1agVAcnKyo5j/lDZNmzkz3l1J0NbNK7cHnb9L7c7knEWNqr4jVGNVSus7QjVB9+H4nbKb6jtCNVEa57zhqbbux30Vrvas7wjVZJRa6jvCTX154uYnIU6/PJ6Xl0dycvk7n68V7K1bt5Kenu7sroUQQogHitMvj//mN7+ptiw2NpZu3bo5u2shhBDigVIvf6ft63vzd0cKIYQQ4tbq7e+0N2/eXF9dCyGEEA2S08+0Fyy4+acunTlzhoED6+bvaoUQQoifA6efaaelpeHm5oa7u3uVfxrNA/MJqkIIIcQ94fTKOXPmTNLT00lMTKyyfPv2uvsoTCGEEOLnwOlFu0WLFrRo0aLa8n79+jm7ayGEEOKBck/fiHbtM1uFEEIIceecfqZtNBr54osvOHDgAIWFhWi1WmJiYpg5c6b86ZcQQghxB5xetBctWkTPnj2ZNWsWXl5eFBUVkZqaysKFC5k9e7azuxdCCCEeGE4v2mFhYcTFxTl+1mq1xMXFcfHiRWd3LYQQQjxQnF60s7Oz2b17N23atMHDwwOr1crp06fJzc11dtdCCCHEA8XpRXvatGksXryYzz//HKPRiJ+fH7GxsUyfPt3ZXQshhBAPFKcXba1WS1JSEiNGjCAiIgKA06dPs2/fPhISEpzdvRBCCPHAcHrRTk5OZufOnSiKQnR0NLNmzSI4OJj58+dL0RZCCCHugNOLttlsZt68eQCkpKQwf/58Zs2ahbu7e422H+XbxJnx7soQT3V9R6imXfv77wvvXb3t9R2hwUg/7lbfEarx8jLXd4Rqnmijqu8I1Zw7UFzfEW5qWsT997kYa07ef/M8zMW1viPcEacX7cDAQMf/4+Pj8fb2ZtGiRc7uVgghhHjgOP0T0c6dO8e+ffscP3fo0IGoqCjOnj3r7K6FEEKIB4rTz7SnTJmC2Vz1MltsbCyvvvqqs7sWQgghHihOL9qhoaGEhoZWW36zLxERQgghxK3d0y8MEUIIIcTdk6IthBBCNBBStIUQQogGQoq2EEII0UBI0RZCCCEaCCnaQgghRAMhRVsIIYRoIKRoCyGEEA2EFG0hhBCigZCiLYQQQjQQUrSFEEKIBkKKthBCCNFASNG+D4T070f8J+/WdwzxANPodLR8+XUCBw2u1xwh0x8neMqj9ZpBiIbM6d/yda/0njIWu81GQKNwNr7zcZW24b97krxLV3DRaNi1dDVDf/s4+ZcziGzfhm/f+wxzvp7Zn7xN3qWrXD2dxo5FK2qdx8XdnZZPzsKamYUtv4CszdsA8G7elLBBA0BRyPh2M0UXL2E6nUapxerYNmxQfzzCw1CpXcjZvhPz2fO1znONys0d34eeojQvk1JjHpa9mwHQRDTHq/tgUBSK9nyDPeMigb/+B2VFJkoLsjGu+hDvfmNQB4WjlNgouXAK69GddRPK1R3tiCcpK8ik1JhP8cHyTOqw5njGDQYULPu+oTTrIh7xw0BR0IQ1x7z2PVz8gvCIHYRSbKHMmEfxsR/qPZNH/HDcWsUAUPTDcuyXTtVJJBd3dxrNeozi7CxK8vMp2LYFAI+mzQgckASKQt7332HLySV4xChKC834dYvn7Ouv4OLujmFfSp3kqEzl7k7I9Ccoyc7Ers/HuON7ANwbN8cvYRAoCvpt32K7fBG1rx9ujZpQnF4+n4MmzKDUZMK9aQvyN6zAdvli3QVzdcd76BOUFWRSZsqn+HB5LnVoczxiy3NZD3xLafZFPLoOQ1HK0IQ2p3DD+7jHDEIdEA4uLhQf+4HSjLN1l4vycYx89BcV41hA/tbycfRs2ozAgQNBUcjdvAlbTg4hI0dRWliIX7dupL32ap3mgPLjgW7CU9jzMinV51G0p3yeuzZqjnev8uNB4c5vKLl6kZBn/0FZoQl7fjb65A/xGzWTskIjrpEtMG1aTsnV2o9f58mjKbWV4NsojF3vflalrdfTv8BwKQMXjZqjy9bRKK4z4dHtcNN6c2jBCory9XSePJpiowmf8FD2ffoVMTPGEdCyGQApHy7AlJF9x5miKzL5NQrjxxsy9Xn6MfSXruKi0XBk2Voi4zoTHt0ed603BxYsJ6BFU7pMHk2xuZCQdq1JfuJ5YqePx1ZUhKfOj9Rvt5FzKu2O8tzzM2273V7nj6nWaGjZrQt7VqzHYjLTLLqjo615TCeKDEb2rFhP6+6xuGjU7FyyipSV68m5cInGHdsBkH8lgwuHjnE1tW6eoKH9+2I8cYpLy1YRPjTJsbzptImkL11B+rKVNJs2CQDLlatVtg0bPJArq9eTve1Hmk6fVCd5rvGMG4Dt/HHMm5fi1XOYY7nPkOmYNy3BvHkpPkOmA1Cqz6E49QC2C+VFx5Z+GrVWh1vj1ri36VJnmTy6DKDk4gmKti3Ds9tQx3LvAdMo3LaEwm1L8R4wHZVWh0fsIFRqV+yZF8rXSZqJUmJF5elNyaWT90UmUCg5fwT7ldOU5l296ePfDf9+CZhPnSR75XKCKp0xh02aQtaKZLJWLids4hTKLEXkrF2FW1AwhampANiysqCsrM6yXOPbqz+W0yfIX5eMrv/1/RQ4bip5a5aSt3YZQWOnAaAbOAL95g2OdfRbvqHgm9UUHtmPtku3Os3l3rk/9vQTWHYk49H1ei6vxKkUbV9K0Q/L8Eqchspbh3uXgeXjl3UBAI/YwVh2r6b46Da8+k+r01wAAf0SMJ86RdaKFQQNGuRYHj55MpnLl5O5YgXhk6ZQZrGQvWY1bkFBjnGsa17xAyg+exzTN0vR9rl+PPAbMR3j10swfrMU3xHlxwN7fg7WkwewnS8/Hph3bMT0/Sqsx/fj0al7rbO4aDREdo3m+MqNFJvMhEe3d7RFxHTAajBxfOVGGsfH4KJR02XGWPZ9tpSzm3fQYewwgtq2JKJLB9TubuSdvQBAqb2UqweOknPyDEX5+rvK1LhrNMdWbqTYVHhDpo5YDSaOrdxIk/guuGjUxM4Yx97PlnBm8w46jh1G7ulzfP3yP/nu9bc4u2UnxaZCMo+n4h0UQHjndoS1b3Pnme54i1pav349AJs2baqTx+s+fiTjX3oOpUwBoLBAjy48xNGuCwuhsEAPgM1ixdvPD2N2Li5qNf4RoZzeuReA1W+8S8qqDSQ+MgUXjbrWuTxCQ7BV9Ovi7n59eUgwJXoDJXoDHqHBN932zHsfEzFiCL5RbXDz19U6S2XqgFDKTOW5VK7ulZaHUGbSU2bSow4o//5zw4r3KNq5Ac+Yfqh1wWgHTMCw+iPyPngJy4FtdZfJPxTFXFA9k38oilmPYtaj9g9FExSJSq3Bsnstbi06ow4IRxPWDPuVNKwpG9GO+tV9kan4yHYsu9ZSfHQHPqNm11kmt+BQ7AY9AC5u7pWWB2M36LEb9LiFlM/9suJirn45H59OnVG5udVZhhu5BodSWpFJVSmTa2AIpUY9pUY9rsEhuAaFUGooQLHZHOvY83IA8GrbAcPWb+s0l9o/lLJCfUWY67lcdCEohXqUQj0u/iGogyJB44o1ZR2uzTvhEhCOef3/8Og2AtfGUbhoA+o0F4BbSAh2fXm2yscGt+AQxzi6VxrHK198jk9n54yjJrDS8cDt5scDTWD58aBg6XuYf9iAV1w/1P7BlBaUj5976w4U7vi6Vjk6jBvGgD89jaKUv7C0FBjwqXQc14aGYCkwAFBiseLh54urh0f5lcGKdQOaN8ZWVMSJVd/QfswQ1G6unFr3HSfXfkfeuYt0mfrQHWXqNG44SX/6HYpSXluKCvT4VsrkExpMUcUx/lomTUUmS4Een/AQrEYTdouVFv16cP6HPRWPO4wf3/mMDXP+RnrKwTveV06/PD5v3rwqP2dkZHD27FmuXr1KUlLSTbfZtGlTjYv6nuXr2Lf6a6b/4yUAvP11XDh4zNGuz8ymaXQHANw8PSg0GFBrNPSZNp5v3/8cn8AAFBSUsjKs5kLsJSW4urlRbLfcza/rYM3KdhTcsuLi68uzc3DV+VWsk3PTbUsLi7i4aBmejSLwadOqVjmqPXZ+Fi4+5bmUkuJKy7Mdy0vzs1B5eKPWBWG3FKJYzLh4++Ki9aOsyAQuLviOfozcf/++bjIVZKHS+lfPVJCFSqtz/L/MVECZ1QxAmcWEysuHMmMeirWQMosJFy+fOslT20waH39KLp6grNCAi49/nWWy5WSh8Svvu8xWXGl5jmO5LTsbjyZNKCsuxpaVRZmtGBd3d0orFcu6VJKThbqib6VSppK8bNS+uop1svFs2xEXL298evTFLSwSj+atsZ4/Q8DICeRvWIGLhwelZmOd5SotyMLFW1cR5nquMn02qorlZQXZlJnzUSzl46dYzLh4+lBWaMCydRHqwEZoGt35mdDt2LKz0egqMhRXHsdsxzgW3ziOxc4ZR3tepeOB7ebHA3teFipPbzT+QZRYCikrKj8elBbk4DN4IsbvlqPy8ITCux+/4ys2cnLNdwz9+x8B8PT3I+PQcUe7OSubsM7lZ7munh5YDUZKLFZQqfDy98OUkU1RXgE2c1F5/mIbGnd3dE0bkXUsFaveiGeA7o4yHV2xgeNrvmV4RSYvfx1XDl2vLaasHCKqZbKASoWnv67Kpfiwjm3Z+d/5AGg8PCgtKcFN602XKWPYMvfO3s+kUq69jHCSv/71r4SGhtKqVXnxOXDgALGxsezfv5/nnnvutts/16lfjfq58Z5258GJlFisnPxhd7V72jPffB27zUZxkYXMtPOc3XuQ3lPGcvnkabx1fnz/ycKf7GuI5+3PxG+8px3UszvH//pPvJs1qXZPO6hPD9r85ilOv/MBuT/uJnL8aFx9fXBxdePyijUU5+bdtr927U012k833tP26NSbgs/+jCa8WZV72mVmA7rJz2A9tQ/X8OYYlr2De7uuuEfFodK4UnI5jaJdP/3q2tVbVaNMN94/du/QC+PCv6AObVbt/rF2zG8ozb6IOqQp5tXvomnUGs/4YZQVmbCdPURJ2p2/cq3rTN6DHkGxWXDxCaD4+I+UnD962+7Sj9/+LOrGe9q67j04/8+5eDRpWuWetspFTcjohyg6m4Z7WDiXP/kQ18AgIh97AkVRuPLJR5Tk335OeXnd/lbWjfe0fbr24sq//4p7ZLNq97RVGldCZv4S94hIMj58i8DRk3ALa4Rdn09Jfi45iz65bX8hNa2hN9zTdmvfC9NXf0Ud0qzaPW3vkb+iNOcS6pAmFK79L569x6Hy8kWlccPy4wrKjLk/2dW5A641DFXuxnvauu49OPfGXDybNK1yT1vloiZ0zBiKzp7FPSycSx9/eEf9hEZYb7vOjfe0vbr0JvfDP+Ma0azKPe1Sk4GAGc9gPbEP10bNKVj0DgEznkET0ohSQz6l+lz0yz+6bX9rTv70i9gb72m3GtQPu9XKhR9Sbn9Pu8BAwh9+Rc7JNLxDAtn70SL6/P5JCi5cwr9pJMeWb0CffqVan6X8dAm88Z5260H9sFuLOf/Dntve0y7K1xPYqhmBLZpy+tvyq5NRwwagDQnCNyKUM5t/4FLKoZv2+/vDNz9xdXrRVhSFNWvW4O7uztChQ1m1ahUPPfQQx44do2PHjrfdvqZF+16qSdG+12patO+lGhdtUaOifa/VpGjfazUu2vfQnRbte6UmRfteu13Rrg+3K9r15VZF2+n3tFUqFWPGjKFx48Z8+OGHWCzll51rUrCFEEIIcd09+5OvDh06EBkZyf79++9Vl0IIIcQD5Z6+e9zPz48BAwbcyy6FEEKIB0a9fSLa5s2b66trIYQQokFy+uXxBQsW3HT5mTNnGDhwoLO7F0IIIR4YTj/TTktLw83NDXd39yr/NJoH5hNUhRBCiHvC6ZVz5syZpKenk5iYWGX59u3bnd21EEII8UBxetFu0aIFLVq0qLa8X7/77++vhRBCiPvZPX0jWpkTvrBACCGE+Llw+pm20Wjkiy++4MCBAxQWFqLVaomJiWHmzJn4+vo6u3shhBDigeH0or1o0SJ69uzJrFmz8PLyoqioiNTUVBYuXMjs2XX3DUhCCCHEg87pRTssLIy4uDjHz1qtlri4OC5erMMvvBdCCCF+BpxetLOzs9m9ezdt2rTBw8MDq9XK6dOnyc396W/OEUIIIURVTi/a06ZNY/HixXz++ecYjUb8/PyIjY1l+vTpzu5aCCGEeKA4vWhrtVqSkpIYMWIEERERAJw+fZp9+/aRkJDg7O6FEEKIB4bTi3ZycjI7d+5EURSio6OZNWsWwcHBzJ8/X4q2EEIIcQecXrTNZjPz5s0DICUlhfnz5zNr1izc3d2d3bUQQgjxQHF60Q4MDHT8Pz4+Hm9vbxYtWlTj7V+d3cgZsWpl8Qf335voTh/0r+8I1Yzvqa/vCDf1m62l9R2hmt/o7r/P4m8/vXV9R6jmy3ev1HeEamYv+mV9R7ipX0+eV98Rqmmnru8E1bVzv/+OBz/F6Z+Idu7cOfbt2+f4uUOHDkRFRXH27Flndy2EEEI8UJz+8n7KlCmYzeYqy2JjY3n11Ved3bUQQgjxQHF60Q4NDSU0NLTa8pt9iYgQQgghbu2efmGIEEIIIe6eFG0hhBCigZCiLYQQQjQQUrSFEEKIBkKKthBCCNFASNEWQgghGggp2kIIIUQDIUVbCCGEaCCkaAshhBANhBRtIYQQooGQoi2EEEI0EFK0hRBCiAZCirYQQgjRQEjRFjU28PUXaDNiYH3HuG+FNWnMv9cvr+8Yt6TRaum9YVV9xxC3kKc388y/FrN628Eqy9/5ahP/W/Z9PaW6vZ7jRvKr//2rvmM41PdxKqR/P+I/eddpj+/0r+a8F6wlZXyw5yphPm4EermS1NofgHN5Fr49XYAKGNI2gDAfN+Ztv0yXRt5kGG08Fh/O1rN6ikpKOZlVRPcmvvRp7ndXGdpPHk2prQTfRmHsffezKm3dnv4FxksZuGjUnFy2jvC4zoREt8NN682xBSuw5OurrRP3y4exFVnw0Ply7tttuGm96TB5DDZzIUHtWrHuiRewmQprnK9TpXy7b8jX8+lfYKjo+9iydUTEdSYsuh3uWm8OVeTzb9EEd1+tY5s2IwaiUqlo1LUzR79aQ86ptLvabwC4uuMz6klK8zMpM+ZjPbAZAE14czy6DgZFwbL3G0qzLuLZfRiKouAa3hzT6vdwj07Axd0L16btKD65h+JjP959jhsMnTaJEpuN0MhGLHr7v1Xapv/+N2SmX0aj0fDNV8kA9ByaREFOLgAd4uMYOm0SRSYzLdpH8cqspygymav1cSdc3N1p/sSjWDOzsOUXkPP9VgC8mjUldNBAFEUh+7vNlNlsNJowlqL0y7gHB3Hh4/LxDhsxFMuVq7XKcCNLSSkf7rxMmI8bAd5uDGobCMC53CK+OZWLSqViSFQQYb5uvLX1Il0a+ZBhLOaxHpHkmG18l5qHh6sLQd6u9GsZUOs8HSaPpsxWgk+jMFJumOfdKz3Hjlc8D8MqnodHFqxA7eZK04SeKKWlBEW1Yvtf/o1/y6ZExHXGRaMm+1gqWUdO1jqjpdjGO0s2ExGkI1CnZVivTgBYbSX07tK6yrr5xkIuZOTSMjKk1v3eSr8p47CXlBDYKJy1//mwStvoZ35J7qUrqDVqfliyim4jB6NSqWjdLYbti1dwOTWN1N376DosqU6y1PVxqsvD4ym1leAZ6M/x5HUUZufdVS4Xd3daPjnL8dzL2rwNAO/mTQkbNAAUhYxvN1N08RKm02mUWqyObcMG9ccjPAyV2oWc7Tsxnz1/VxkcWWq19X3i+7MFdAj1ZnJ0CBtP5TuWLzyYzeToYCZFB7PwYBYXCqxo1CqGRwVyOsfCFUMxiS11hPu4k19kp22w513176LRENE1mtSVGyk2mQmNbu9oC43pQLHBROrKjTSKj0GlUdNxxlgOf7aUC5t30HbssJuuk308Fa+gAEI7tyO4fRvyT59n68v/ZPvrb3Fhy847KtguGg2NukZzoiJfWKV84TEdsBpMnFi5kcj4GFw0aqJnjOXAZ0s5u3kH7ccOA6DFgD6c37LTsd3p9ZspNplRu7mhv3j5rvbbNZ4xAyi5cIKircvwjB/qWO49cBpFW5ZQtHUp2qTpuGh1eMQNQqV2xZ5xAYDiw9uw52fg4uNPyaXUWuWoTOOqoUN8HJuTV1FoNNE2prOjLSq2C2a9kc3Jq+jYoxtqjYZmUW24mHoGpawMgIupZ3j3j6/w/st/IeX7bbUu2ADB/fthPHGKK8krCR1y/SDZeNokLi9bzpXkFTSeOokSg4ELn3xOxuq1+HXuCIBbYAClRUWUWi21zlHZljP5tA/TMjk2nI0ncxzLF+zPYEpsOJNjwli4/yoX861oXFQMbx9ManYhV/RWPk+5grvGhcLiUtqFan+il5q5Ns9PrtyI7YbnYVjFc+xkxXPMRaOm84yxHPxsKec276Dd2GGYM3M49912fCPDyTx0HIDYx6ZSUmTBRaMh/+zFWmcE+Hb3cTq1imTG8J6s3X7IsbxRiD9qF1WVdVdtOcCYhNg66fdm1BoNrbvFsHP5WiwmM827dHS0tYzpTJHByM7la2kTH4eLRs3edd9SZDTj6uZO9oVLKGVl5F/NrJMszjhONe/fi6NL15J19CQdxg2/62yh/ftiPHGKS8tWET70+nOv6bSJpC9dQfqylTSbNgmg2gvjsMEDubJ6PdnbfqTp9El3neEapxdtk8nk+P8PP/zAe++9x8qVK7HZbHXWR5apBJ1n+UWDYnuZY3m2uXy5zlNDlqmEqBAvmvl7sPp4LmWKQoHFDkBMIy1jOwax/mT+TR//p0SNG0afPz2NopT3ay0woA2//qpYGxqCtcAAQInFioefLxoPj/Kzx4p1b7ZO1Lhh7H3nMzbP+TtXUg5RbDRht1hp2q8H6T+k1Dhf+3HDSKyUz1JgwOeGfJYb+natlM8nPISQjm3JPnYKRVGqPPaFbbu5vOcgzRJ63PF+q8zFP5QycwEAKlf3G5brKTPrcfEPRR0ciUqtwbJrLa4tO6MODC/PnXaIoh2r8OwxolY5rkmaOJYnX/l/KGXlv6+xoICg8DBHe1B4KMaC8rzFFgs+Oj/iEvtyYNsOxzpmg5Fii7Vi+Q91kss9JIQSvR4Atfv1/eQeHEyJ3kCJ3oB7aAilRRZKi4rwbtmC3Iq+w4YNIXPjt3WSo7JMkw3/mz33TMXXn3tGG1Gh3jQL8GT10WzKFMi3lHAh30LrYC+Gtw/ivz+k1ypHu3HD6HcH89y90vOw8nPWkq9n978/ofWw/gAEt2/N5d0HyDh4jOgZ42qV8ZrMXAMBvt4AFNvst1wvI1dPgK837m7OuSDaa/woprz8vON5bc7XExAe6mjXhYVgztcDYLNY0fqVX4U8tu1HTu3eS8fEXnWWxVnHqV3/+ZTOU8fg37wJnoH+d53PIzQEW4EeKD/rdiwPuf7c8wgNvum2Z977mIgRQ/CNaoObv+6uM1zj9KI9b948ALZv387+/ftp1aoVJpOJTz/99JbbbNq0iTlz5jBnzpwa9RHq44q+ogC7a67/SiHa8uV6i51QH1cA+jX3Y0yHIHzcNUT6ubProhGAIG9XcgpL7vj3O7ViIzv+8h9UqvJ+Pfz9MGdkO9rNWdl4+JdPdldPD6wGI3aLFVQqPCvWvdk6Gg8PykpKcNN606nSwSK4YxtyT5yucb4TKzaytVI+T38/TDfk87yh75JK+UwZ2QS3bYlv4wgiu0UTGd8F30ZhNOnVFYDCnDy8gwPveL9VVlaQhYu2/AmllBTfsFyHi1ZHWUEWZaYCyqzlZ6xKkQmVlw9u7ctfMJQaclH7BdUqxzWblq3kw9f+hqrirMfX35/cjOtnE7kZWfj6l+d19/SkrLQUTy8vBk8eT0BoCL2GDnKs26pTB84eq/0lVYDi7GxcdToASouv76finBxcdX646vwoziofW69mTdG2aU3Olu2o1GpcA/wJHzUc9+Bgwkfd/RnHjcJ83Bwvfqs893zcrz/3fN0A6NvSnzGdQvB1VxPp50GAlxvebmp83DUYi29dvGri5IqNbL+DeV5c6Xl47TkbFtMBjWd5IVCp1QBY8vTYCosoNppw9/OtVcZrwoL8yDeWXyn7qYJ8+PQlzJZivt97kqNpVzh1PqNO+r9m5/K1fPX6v1Cpyue5NkBHfkaWo12fmY02QAeAm6cHZoOBdr3iATDm5OEXXDfPN3Deccqcmc2RRavIPXWWgvN3/8LQmpXtKLhllZ571uzrzz1rVs5Nty0tLOLiomXoDx+j8OKlu85wzT27p52bm8szzzzj+HnJkiW3XDcpKYmkpPJLEKb/Tb3tYw9o6c8He66SV1TCsKgAXvvuIi8lNWF6TAhLDuegAqbHlL+CnLslnYSWOtqHeqHz1LD/solMk43z+VbGdry7SVhmt3N132Hajh2Gu4+WrMMnaD6oH3arlUs/pNCkb3fajh3GlZSDKPZSji1cSfSjk6rc075xndNrvqXzIxPxiQjj/ObysyX/Vs3IP3Pn90PK7Hau7DtM+4p8mYdP0LIi38UfUmjWtzvtxw7jcspByuylHF64kthHJ1W5V+Sh8yW0Y1uUMoViUyHN+/fCOziQ4PZt2P/xorvab9dYDn6Pz6gncfENwJLyNX4Pv4RhwV8o3LwIr/6TQVEwb1pIae4VSnOu4tlrFLi4YL90Gp+Hfo06IAxNWDOKdqyqVY7K7CV2jqfsZ+CEh/D29SH14BF6Dkmi2GrlwLYdxCX2YeCEhzi2ey/GAj0L5r1DdO8eKIqC2VB+RtC4dUvST9fiXv8NcrZsp/kTj+IW4E/WN5uI+r8/cOpvb3Bp0VIiJ45HURQuLV6KR3gY7V9/CeOxE4QNG8zx//cKZ//zP3w7dgBFocRoun1nNdS/dQAf7rxMfqGNYe2Cee3rNF4a0pIZceF8dSADlUrF9LgIAOZuOkdCqwDah2nx93JlRtdw1h7LxsdDw4yKdWrj2jxvN3YYbhXPwxYV8zz9hxSa9u1Ou4rnWJm9lCMLVxJT8Tw8smAFAa2aEffENAqz80j/cS8A+z9aSMysSahcXDi+bF2tMwIM7tGBd5ZsJk9vZlS/Lvzx3eX89VdjydGb2XviAi4qFT07tWRor07YSuy8teg7XFQqfLw96qT/ykrtds7sPUiv8aPw9NFy/tAxYgb3x2a1cnz7Ljok9KLX+FGcTtlPmb2U6IH98AsJokmHKL758AsA4oYl4RcSRKu4aNL2H77rLM44TnV5eByWfD2eAf4c+PSru86WteUHWj45C7cAfzK+3kSHP73I8b/+k4uLltFk0jhQFC4sWgpAUJ8euAcFEtS7B7k/7iaoTw9cfX1wcXXj4oJb172aUik3XvOsY6+99hqvvPIKq1at4qGHHnIsX7p0KZMm3f76fk2K9r22+IPc+o5QTd3dbKg743vq6zvCTf1ma2l9R6jmNzqf+o5QTbfHmtd3hGq+fPdKfUeoZvaiX9Z3hJv69eR59R2hmnZqr/qOUE0b99pd5XGWwSlbbrrc6ZfHvby8SE5OJjMzk7NnzwKQnJzM5cu1e/OSEEII8XPj9MvjL7zwQrVlI0eORF1xz0gIIYQQNVMvf6ft4VH392aEEEKIB129/Z325s2b66trIYQQokFy+pn2ggULbrr8zJkzDBwoH4kphBBC1JTTz7TT0tJwc3PD3d29yj+N5oH4BFUhhBDinnF65Zw5cybp6ekkJiZWWb59+3Zndy2EEEI8UJxetFu0aEGLFi2qLe/Xr5+zuxZCCCEeKPf0jWhlZWW3X0kIIYQQN+X0M22j0cgXX3zBgQMHKCwsRKvVEhMTw8yZM/H1rZvP8hVCCCF+DpxetBctWkTPnj2ZNWsWXl5eFBUVkZqaysKFC5k9e7azuxdCCCEeGE4v2mFhYcTFxTl+1mq1xMXFcfFi3Xw3rRBCCPFz4fSinZ2dze7du2nTpg0eHh5YrVZOnz5Nbu7996UbQgghxP3M6UV72rRpLF68mM8//xyj0Yifnx+xsbFMnz7d2V0LIYQQDxSnF22tVktSUhIjRowgIqL8O3NPnz7Nvn37SEhIcHb3QgghxAPD6UU7OTmZnTt3oigK0dHRzJo1i+DgYObPny9FWwghhLgDTi/aZrOZefPKv4w9JSWF+fPnM2vWLNzd3Wu0fcnVq86Md1dmvtS9viNUYy/Iru8I1VxeZ6jvCDf117b330fo/nixZs+Heylg2an6jlBNUuvS+o5QTfKM9+o7wk09HnY/fi6Gub4DVJNp8KzvCHfE6UevwMBAx//j4+Px9vZm0aJFzu5WCCGEeOA4/RPRzp07x759+xw/d+jQgaioKM6ePevsroUQQogHitPPtKdMmYLZXPWSSGxsLK+++qqzuxZCCCEeKE4v2qGhoYSGhlZbfrMvERFCCCHErd3TLwwRQgghxN2Toi2EEEI0EFK0hRBCiAZCirYQQgjRQEjRFkIIIRoIKdpCCCFEAyFFWwghhGggpGgLIYQQDYQUbSGEEKKBkKIthBBCNBBStIUQQogGQoq2EEII0UBI0RZCCCEaCCna9SjPXMwLX+1l3aFLjmWbjl3l4fe312Oq+1/QlCcInPgLx88u3lpaff51PSaqnkk0LAGTnsB/fMMYv0YD+9L/8//UdwxxC6H9+9Ljk3ec9vhO/2rOe8LVHc8Bj1Kmz6LMXEDJ8a0AuAQ3xb3zQFAUio9sBrsNr9G/p8yYgya0OeYV/8A9dhgqTx8AVBo3CpP/WufxLDY773+fSpifJ4FadwZ3agRAcUkpPVuFVFm3bbgfnm7qOs/gyFJSyoc7LxPm40aAtxuD2gYCcC63iG9O5aJSqRgSFUSYrxtvbb1Il0Y+ZBiLeaxHJP/edhGD1Q6AzV7GX0a0rpNMKjd3gqY+SUlOJqX6fEw7NwPg1rg5vn0HgaJg3P4ttisXUfv44RrRBNul847t/QaMpCTzSp1kqYtMmoAgfHonUWa1UKrPw7x3x11lUHu4E/vLhzFnZGPJL+DCdz8AoGvRhBZDElFQOLdxK4YLl+jy+DQKc3LxDAzgyKdfVVvHnJlN9+eeIuvgcbQRIRz+eDEthiSijQhFpXYhfesuCtIu3Pl+cncn7OEnsGVnYi/Ix/DD9wC4N2mGLrF8P+m3fkdJbg4Bw0ZTVmhGG9ed9L+9RMDQ0biFhlFms2FJS8W0d9dd7aeb5nJzJ2DSk9hzMyk15GPeVTF+kc3R9h4MioJpxzeUXL2Ii48fbuFNKL5cPn7a3oNwDY4AFxcK923Hln62VlnUHu60f2omRZnZWPMKuLKp/EW5T4smNB7SHxSFS19vwXThElGPT8eSnYtHUACpny4mckgi3hFhqFxcuLptF8a08+hT07BbrLXbQRVU7u6Ez3wcW3YW9oJ89NvLx8+jSTN0/cvHr2BL+fgFDh9NaaEZ37h4Lvz1ZTSBQfgnDKTMaqEkPx/j7rub5w0hk4u7O62ffARLZja2/AIyN28DQNu8KeGD+6MoChnffE/hxUuYTp+l1Hp9fCLHDMczPJSyYhuG1DPk7kypXZZabX2fcGvfD/vlUxTvWYl7dJJjuUfvSVh3Lce6ewUevSdRZs6nMPmvFK36JyVp+yjLuUjx7uUUrfontkPfUnxgo1PybT6RQYdGOqb2bMGGw5cdyyP8vVC7qKqs2zjQ2ykZrtlyJp/2YVomx4az8WSOY/mC/RlMiQ1nckwYC/df5WK+FY2LiuHtg0nNLuSK3srkmDBeHtKSEe2DGN0x5Cd6uTM+PftjPXMC/YZl+CYMdSwPGDONgnVLKVi/jIAx0wDwGzAC45b1jnXU/oGUWYooK7bUWZ7aZgoY+zCKrRi1lzfWs6fuOkOzAX3IOZ7KySVraDV8oGN5xxnjOfHVak5+tYaOD4/HOzQYzyB/zqz+Fu+QQLxCgqqto2saSZndztkNmwls2xJtRBgthiaSunIjF7fspOPMCXeV0a9Pf4pST5C3Jhn/gUMcy4PHTyN39TJy1yQTPH4qZZYi8tavRBMUjOVM+T6xnD2N2leHZ4tWeHeIvuv9dDPa7gMoPnsCw9fL8Ol7ffx0I6dh2LgEw9dL8R81HQDfxJEYt21wrOPTZwiGzasoTNmG/6gZtc7SaEAf8o+ncnbJappUGsc2MyaQ9tUq0paspvWMCXiGBuMR6M/FNd/gGRyIZ0gQTYYO4PzKDVzd8iNtHi4fo8LLGbXOdI2uTyJFqSfJXZ2M/8DBjuXBE6aSu2oZuauTCZkwhTJLEbnrVuIaGEzRmVQAQifNoKy4fJ4Xnbn7ed4QMoUN6IvhRCrpy1YRMfT6GDabNoGLS1aSvnQVzaZPBKDoytUq2xpPp+Hm54dP65b4R3esdRanF+19+/aRl5dHWVkZy5Yt47XXXuPDDz/EaDTecptNmzYxZ84c5syZU6M+XPxCUIr0AKhc3a8v9w1GKTKgFBlw8QsBuw2lyIA6tAX2zPJXz2WG8sLl2jIO+7kDd/lb/rQsgwV/7/Jc1pJSp/RRU5kmG/6e5RdYiu1ljuXZpmJ0nhp0nhqyjDaiQr1pFuDJ6qPZlCmQbykhzLf8d0i5aCC+qV+dZdIEhVJqLADKX2U7lgeGUGrUU2rUowkKRRMUgt2oRymxOdbxSxyOYeuGao9Zn5ncIpthvZCGYevXBM2YfdcZvMOCsRYYAFC7u11fHhqEtcCAtcCAd2hw+c/68ueTVW/EOzSo2jp5qWcxXLhE6zFDULm44BmgY9+7n9F61CCC2rXGM0B3VxndgkOwG/QAuLhd30+uQcGUGvSUGvS4BocCoBQXk73wM7w7RKNydSNw1DiyFn5C+j9exbCrbm8JVRk/t8rjF1p1/ALL11NKih3r5C38H76JI3FvGYXaz7/WWTzDQrA5xvF6Fs+QYGwFBmwFBrzCgvEMDcJWMY42vRHP0GCOvfMJTUcNRte+De53OUY/xS0kFLuhfD+5VMp2bVztBj1ulcYva+FneHfsjMrVDY8mTbGcSyN/09dEPPrUA53JIzQYW4G+WiaP0GBsegM2vQHP0OCbbttk/GjSPv6Cwy//jextO2udxelFe/v27Xh5ebF+/XpKS0sZM2YMLVq04Msvv7zlNklJScydO5e5c+fWqI8yQzYqLx1AlSdfmTEHlZcfKi8/ygzZjuWu7ftScuIHx88uujBKCzLv8DeruVA/TwoKy3N5uDrv0ndNhPm4UWApv8Ttrrk+/CE+7ugtdvQWO6G+5QWib0t/xnQKwdddTaSfBwBXDVYiKv5fV+y5Wah9yw+OSvH18bPnZaP21aH21WHPzcKzTUdcPL3RxvfDo3U73Ju1RqMLQJc0Gk1AMH5Jo++LTKX6fMqKCikrNKH29r3rDIWZOXj4l784Ki2+/qKgMCsXD38/PPz9KMzKKf9ZV96Ph873+rJK6wCkb9vNmdXfUGw0Y7x8lZLCIo59uZysQ8cxnL9UPUAN2HKy0fjpACizXd9PJbk5qP10qP10lORk4d64Ka4hYY71XDw80Pj6UWoygcqF0GmP3lX/t1Jl/GyVxy+ryvh5tO6Ai6c33l374dGqPW5NW1NmLUS/diHWU4exXblQ6yyWzGzcHON4PYslOwc3fz/c/P0oyszBkpWLW8U4uul8KcrMpqTIwpkvk8k7dAzThbsbo59iy85CU/HCpKxStmvjqvHTYbth/JTi8vErKSif56WFZtQ+Pg90JmtWDm7+umqZrFk5uOn8cNP5YcnKuem2bn6+lJhMqFxUtHys9lduVIqiKLV+lJ+wbt06Ro4cydq1axk1apRj+YoVKxg3btxtt8//U8LtO7nhnrZbm+4UrnoDl6AmVe5pl+Wmo/Lyw61zEsW7lzs290iYgfXHpWC3/UQn13l16V6j9a658Z72jtPZvDq2C7lmK+9+dwoXFfw6qR3Bvh5sO5XJ218f59mhHegXFVbjPuwF2bdfier3tHeeL+ClIS25kGepck+7eaAnv1txkoRWAZSVKUzoUp7l092XmRYXXqMXH5fXnahRphvvH3vH9STz3b/iFtms2v1jlcaVoBm/xC28MVkfz8Oek4lH206E//b/yFnwPubdW2vUpzMzqb198E0cSlmhmaLjh7CcOHjb/n68qKu27MZ72o17d+OH195C17zxHd/TNly4xKD/vE761l2oXFw4lbyeqIkjcffzQe3qyqnkdRTl5Ffpv2vIra+GOfbTDfe0fbv15NJbf8O9cdMq97RxcSFoxDgs59NwCw0nc/77aLvE4d05FheNK5YLZ9F//81t+/PyrdmVqhvvaXvF9CL7vb/g1qhZtXvaKo0rgVNn4xoWSc5n8/CO7Y2L1heVqxuGb5dTWpD7k30dPvvTV51uvKcd1jue/a+/iU/zxre9p91i4ijcfH1wcXPlXPI6rDl5hPftTqdnnuTIWx+SuWPPLfttGVh4+/10w/1j3/iepL/5NzwaN61y/1jl4kLQqLFYzpWPX8ZnH+DZohX+SUMpNZswHz1M4dFDt+2vJu51pkyD523XufGednCveI7+5V9omzWpdk87uE8Pon77JKf+8wE5P+4hoGsMgXHRqFxdMaed4+rXm2u0H4bt/f7m+8fZRXvZsmWMGjWK7du3ExsbS1BQEBaLhS+//JInn3zyttvXqGjfY3datO+Fmhbte6mmRVvcvGjXt5oU7XutpkX7Xrpd0a4vNSnaomZFuz7cqmg7/d3jgwYN4t133yU3N5fPPvsMX19fPDw8+NWvfuXsroUQQogHitOLtk6n4/nnn0ev15Obm4unpyfh4eG4uDwQb1wXQggh7pl79nfaOp0OnU53r7oTQgghHjj1drq7eXPNbsYLIYQQopzTz7QXLFhw0+Vnzpxh4MCBN20TQgghRHU1OtN+7bXXWLRo0V11kJaWhpubG+7u7lX+aTQPxieoCiGEEPdKjSqnXq9n6tSpd9XBzJkzSU9PJzExscry7dvlSzGEEEKIO1GjM+2oqCgKC6v/zd9///vf227bokWLagUboF+/fjXpWgghhBAVanSmbbFYeO6552jdujVeXl6O5UeOHLmjzsrKyuRPvYQQQoi7VKOifebMGZKSkqotr8l9aaPRyBdffMGBAwcoLCxEq9USExPDzJkz8fW9+89lFkIIIX5ualS0x44de9OiHR4eftttFy1aRM+ePZk1axZeXl4UFRWRmprKwoULmT377r8BSQghhPi5qdG16psVbICLFy/edtuwsDDi4uLQarW4uLig1WqJi4sjNDT0zpIKIYQQP3M1OtO2Wq2sXr2a8+fPU1zpa8kuXLjA9OnTf3Lb7Oxsdu/eTZs2bfDw8MBqtXL69Glyc3/6m3OEEEIIUVWNivaHH35IeHg4WVlZjBkzhtLSUg4fPkzXrl1vu+20adNYvHgxn3/+OUajET8/P2JjY29b7IUQQghRVY2KdkFBAU8//TTHjx93/PnWwIEDefPNN2+7rVarJSkpiREjRhAREQHA6dOn2bdvHwkJ99/XbgohhBD3qxoVbbvdDoCiKOTm5hIUFITZbCY9Pf222yYnJ7Nz504URSE6OppZs2YRHBzM/PnzpWgLIYQQd6BGRbtp06Zs3LiRvn378txzzxEREUFmZiY9evS47bZms5l58+YBkJKSwvz585k1axbu7u41Cvjemvvv404PJcuXndREgd1a3xFuKtTV6/Yr3WOuKkN9R6hmQ3pJfUeoppHm/hu7wjJzfUe4qasXq38gVn27H597kRqlviPc1LBbLK9RRXz88ccd/w8PD+fs2bOEhYXRrVu3224bGBjo+H98fDze3t53/TnmQgghxM/ZHX08mV6vR6vVMnLkSLp27YpKpbrtNufOnWPfvn2Onzt06EBUVBRnz56987RCCCHEz1iNinZBQQF//vOfeeqpp/jXv/6FxWLhD3/4A2lpabfddsqUKfj7+1dZFhsby6uvvnpXgYUQQoifqxoV7Y8++oju3bvzySefEBQUhLe3N3/6059YvHjxbbcNDQ2lZcuW1Za3aNHiztMKIYQQP2M1/sKQwYMHV1nm5+dHWVmZU0IJIYQQoroanWmXlJSQmZlZZVlubq7jT8GEEEII4Xw1OtOeOHEif/jDH2jZsiVXrlxh7ty5pKWl8fTTTzs7nxBCCCEq1KhoHzlyhH/+85/s2LGDiIgIAgMD+cUvfkFISIiz8wkhhBCiQo2K9tatW8nOziYmJoahQ4fi5XX//YG8EEII8aCrUdEeM2YMw4cP58CBA3z00Ue4uLjQq1cvYmJicHG5oz/1FkIIIcRdqlHRHj16NFD+iWbx8fEcOXKE//73v5SWltKrVy8SEhJu+mddQgghhKg7NSraH3zwARMnTuSHH35g+/btFBQU0KNHD/r164ePjw9bt27lu+++45e//KWz8wohhBA/WzUq2teKdadOnZgwYQJdu3bF1dXV0T516lRefPFFp4UUQgghRA2LdlhYGC+//DK+vr43bd+5cyddunSpy1xCCCGEuEGNivbf/vY33Nzcbtnep08f+vTpU2ehhBBCCFFdjd76/VMFWwghhBD3hvy9lhBCCNFA/CyLti4ynKkfvEFkTMf6jlJFWJNI3lqfXN8xqrgfM92vQptE8sa6pY6fE8aP5oUP3q6/QPepsCaN+ff65fUdQ4gGqUb3tBuCuMljKC0pQdcojK3vfFqlLfHpx9BfzsBFo+bA0rUAXD50rMo60z9+E/3lq2SlnmXf4lV1kmnItImU2EoIjYxg8dv/q9I27fe/ITP9MhqNhm+/Ki+KPYYOQp+T61jnlc8/ICv9MhdST/P1giX3RaYh0yZSaDQRFBHOqg8/q5NMo2dMpcRmIyyyEZ/N+0+Vtseef4ar6ZfQaDSsXbSE8Y/OpGnr8s8EWPDu+5SWlvLUnBc4sHMXEU0a8+mb/66TTACDpk2kxGYjuFEEy/79XpW2yc/+mqxLl1FrNGz+qrwAdR+S5NhXKhcXju/aS68RQ+osD8DAqRPKM0VGsPzf71dpm/Dsr8hOL8+0ZckKeo4aigoVUfGxbF6UjCEvnykvPM2JXSkEN4lk+dvv3aKXOzN02iRKbDZCIxux6O3/VmmbXmlOfVMxp3oOTaKg0pwaOm0SZqOR4IhwVtbRnOo7ZRz2EhsBjSJY/58Pq7SNeuYpci9dQa3RsGPJKkb89gnyrlylcfsoNv7vE1QqF4b+chYXjp7AU+vN9kV18wKj/9Tx2G0lBEVGsPKGsRv37Gxy0sszbV2ygh6jhqIC2sbH8v2i5aSfTKX/1PEUGU0ERoSx4aMv6iQTwMjpUxzPv8/feqdK26znfkdG+iU0rhrWL1rKQ7Medjz/Fv/3A7KvZjD3y0/ISL/E+VOnWfPlojrJVJt5rs/JYfQvH+PskWN4+WjZtHBZnWS6pvvkh7CX2PBvFM6mdz6p0jbo6cfJv3wVtUZDytI1JP3mFxRcySSifRu+f28+hfn6Wvf/QJxpu2g0NO0WzaEVG7CazDSKbu9oi4zpiNVo4tCKDTSLj8FFo0Z/OaPaYxiuZHD50HGyTp+tk0waVw0d4rvyffIqCo0m2sR0drRFxXbBrDfwffIqOvbohlqjoVlUG9JTz1T5utPsy1dIPXiYi6dO3xeZmkW1ISq2C65ublw6k1ZHmVyJ7t6NjUuXU2g00T6mi6OtY1wsJr2BjUuX06Vnd9QaDXa7naN7D5B2/CT6vHz0efkUWy34+fszcPTIOskEoHbV0K5bLFuTV1NkMtG6y/V91SY2GrPBwNbk1XTo3hW1Rk3TqDaknz6DoigAKGVl5F6tPs9qmykqPpbty9dQZDLTqksnR1vr2GgKDUa2L19D+x7lmXat/ZoikwlXdzcyL6Zjys/HZrWi9dfRc2TdvJgon1NxbK6YU22rzSkjm2+YUxdTz6BUmVPRuLm5c+lM3Tz31BoNrbrFsGv5OiwmE827XL+i1iKmM4UGI7uWr6N1fBwuGjU7lqxk94r1ZF9Ip2mn9jTv0pHcy1fZu+ZrYoYORFPpz1vvOlPF2P2wfA1FJhMtK41dq4qx+2H5Gtr1iEOtUbN77dcUmcy4uruTdTGdxlGtaRXTGVd3N66cOVfrPNdoXDV07t6Nb5atwGwy0S4m2tHWIS4Gk8HAN8tWEN2j/PlXardzfN/15x9A1uUrnDx4iHOnUuskU23necvoTuRcvsLONRuJHzqoTsbPkU2joXm3aPZX1JrG0R0cbU1jOmIxmti/YgMtKmrNnqVr2L9yA7kXLhHZMapOMji9aL/22mvs2LHjjr57e9OmTcyZM4c5c+bcdt0u44cz7E/POA4CRfkG/MJDHe2+YcEU5RsAsFmsePrd/M/Wvvvnexxe9TU9HpmEi0Zd46w3M3DiWJ545Y+OTMYCPUHhYY72wPBQjAV6AIotFnx0fsQm9uXAth1VHufzv89jy4o1jP7Fw6g1tbsoUheZIlo0w1JYyJYVa+g/djSutXyD4vDJE/jd6y85MunzCwiJCHe0B4eHoc8vAMBaZMFXp2PTyjV8t3I1F9PO8tDM6bTr0pmrF9NZ8uEn/P25P9QqzzX9Jz7EL16Z4yjApnw9gRHX51RgWCimilfMxRYrWp2OmMQ+HNr2Y530fzMJE8bwyMt/cDyPTPkFBFYav4CwUEwV+6q4yIJW5wfAoa07OL5rL10S+tAyuhNZ6ZfZ8MmXfPiHV2udKWniWJ585f+hlJXvJ2NBQZU5FRQeirGgIlPFnIq7YU41atEMS2ER369YXSdzquf4UUx6+XkUpXw/mfP1+Fc6HujCQjBXjJ3NYsHbzw9Ddi4uajUB4aGc+jGF49t2olKpiB8zDKWsDE9fba0y9ZswhodffpGyiv1kztcTWClTQKVMxUVWvCvG7vDWHZzYlUJ0Qm/CmzfFWlTEjhXr6D12JJo6eHPw0Enj+c1rLznmlOGG519QeBiGa88/iwVfnR+bV61l08o1pKedZfTMaQB88Nc3+DZ5FeMfe6TWx6m6mOeHt/8IKhV9HhqBopTh5etTq0zXdB0/gtF/etYx3wvz9ejCr39pll9YiONM2max4uXniyk7Fxe1C7rwUNJ27quTHE4v2na7Hbvdzj/+8Q9WrFiB2Wy+7TZJSUnMnTuXuXPn3nbdQ8s3sPEvb6Oq+Ax0rwA/DBlZjnZjZg5eAeUD6+bpgcVgrPYYPqFBuHp6AFBaYq/1E2LzspV89NrfHZl8/XXkZlz/PvK8jCx8/XUAuHt6UlZaiqeXF4MmjycgNISeQwcREBqCu1d5JntJCa7u9Z/JkJePxVwIQInNhpuHe60ybViSzL9f/rMjky7An+xKZ6c5GZnoAvwB8PDyxKjXE9miOVD+okMXGICfvz+mijEdM2MqvhVP4trYsmwVn742F5VKBYBPgI68q9fnVF5mFj4BOgDcPT0oKy3F3cuLgZPH4R8STPehSbXOcKNtyav5/PV/OD7r3yfAn7xK45efmYVPxb5y9/LErDfQsXd3AAw5uehCgtDq/Cgylu+rgVMn4H2LF7A1tWnZSj587W+oXMr3k6+/f5U5lZuRha9/RaZKc2pwxZzqVW1OFdd6Tu1avpalr/8Llap8P2kDdBRUOh7oM7PRVoydm6cnhQYDao2GhBkT+fr9+fgEBaBSqUhZs5GU1RspMhgdBfVubU9ezZevv4FLxX7SBujIq5Qpv1Imdy8PCvUGOjjGLg9dSDCGvHys1/ZTce33E8DXS5fz7it/dswpvxuef7kZmfhde/55emLUG4hs0QwAk96ALiCAoLBQPLw8gfLjlFstj1N1Mc9VqPhx9QZ2rFpPocHoKPK1tW/5etb85S3HfPcO0KHPyHa0GzKz8XbMLQ+KDEbUGg09p49nywefow30r5McTr+nrdFoSExMJDExkePHj/Pxxx/j7e3NsGHDiIyMrJM+yux2Lu49TJdxw/Hw0XLl8AmiBvXDbi0m7Yc9tOrbnS7jhnMh5SBl9lICmkUS1q41Gjc3sk6l4eGjJW7KGDJPniHzxGlsRZZaZ7KX2Dmeso8BEx7C29eH0weP0GNIEjarlQPbdhCb2IcBEx7i2O69GAv0LJz3Dp179wBFwWww4O3rw9Bpkzh34hRnj5/CWlhU75lOpOyn+6D+JI4bxaW0cxQaTXWQqYTDe/YybNJ4vH19OHHwEP2GDabYYmXP1u1079+PYZPGc2jXHkrtdhKGD6Flu7ZENmvK+sVLyc3K5qk/vsDwyROwFdsw6g21zgTlL95O7j1A4oQxePn4cObQEeKHDMRmtXJo24/EJPQhccIYju/Zh6lAz5J579KpV/eKfVVeGHsOH4x/cBBRXWM4te9gnWQ6lXKAfuNH4+WjJe3QUboNGYDNUszh7T8SndCbfuNHc2L3PkrtpcQOTEAXEkzzDu1Y+8FnFJlMTH7haRImjKGkuJjCm7yAvVPlc2o/AyvmVOrBI/QckkRxxZyKS+zDwEpzasG8d4ju3QOlYk4dT9lP90ED6D9udJ3NqVK7nbS9B+k5fiSePj6cP3SMLoP7Y7NaObF9Fx0SetJz/EjOpOynzF7KL+b9mRKbjVHP/pKMM+c4+M33TPy/50jbe5B96751XHGpVaaKses7fjRePj6cPXSUrkMGYLNYObJ9J50TetF3/GhO7t5Pqb2UmIEJ6EKCaNahHes/mI8hN4+4Qf3pPXYEGWcvUFQH+wnKx+/Inr0MmTgOrY8PJw8eps/QwRRbLezd+gPdEvsxZOI4Du8uf/71GzaEFlFtadS8KRu+Woa3jw+jZkwh7fhJzhw7gaUOjlO1neeePlpmvvQCp1IOsHPt13Uyfo5sdjvn9x4mrqLWXDp8nA6DEiixFnP6h9206duDuHHDOVdRa6a8+Sr2YhuDf/ckWWnn2fPVqlpnUCl1+RvdxGuvvcYrr7xSZVl2djYbN27kkUceue32f+080FnR7tohm76+IzQIBXZrfUe4qVDX+++rZV1V99/bS0xlJfUdoZpGmvtv7ArL7PUd4aaulhTWd4Rq7sfnXuR9OKcA/nJky02XO/1IkZGRwa5du6osCwkJqVHBFkIIIcR1Tr88/v77799+JSGEEELc1v13TU4IIYQQN1VvRXvz5s311bUQQgjRIDn98viCBQtuuvzMmTMMHHj/vclMCCGEuF85/Uw7LS0NNzc33N3dq/zT1PKP8IUQQoifG6dXzpkzZ5Kenk5iYmKV5du3b3d210IIIcQDxelFu0WLFrRo0aLa8n79+jm7ayGEEOKBck/fiHYnnz8uhBBCiKqcfqZtNBr54osvOHDgAIWFhWi1WmJiYpg5cya+vrX77GMhhBDi58TpRXvRokX07NmTWbNm4eXlRVFREampqSxcuJDZs2c7u3shhBDigeH0oh0WFkZcXJzjZ61WS1xcHBcvXnR210IIIcQDxelFOzs7m927d9OmTRs8PDywWq2cPn2a3NxcZ3cthBBCPFCcXrSnTZvG4sWL+fzzzzEajfj5+REbG8v06dOd3bUQQgjxQHF60dZqtSQlJTFixAgiIiIAOH36NPv27SMhIcHZ3QshhBAPDKcX7eTkZHbu3ImiKERHRzNr1iyCg4OZP3++FG0hhBDiDji9aJvNZubNmwdASkoK8+fPZ9asWbi7u9do+z3WPGfGuyvdPQLrO0I1pSj1HaGacy7350fV+ru41XeEavxdXOs7QjUbTJfrO0I1AeqaHTcEBGg86jtCNSXK/fdZHak2Y31HuCNOP6oGBl4vcPHx8Xh7e7No0SJndyuEEEI8cJz+iWjnzp1j3759jp87dOhAVFQUZ8+edXbXQgghxAPF6WfaU6ZMwWw2V1kWGxvLq6++6uyuhRBCiAeK04t2aGgooaGh1Zbf7EtEhBBCCHFr9/QLQ4QQQghx96RoCyGEEA2EFG0hhBCigZCiLYQQQjQQUrSFEEKIBkKKthBCCNFASNEWQgghGggp2kIIIUQDIUVbCCGEaCCkaAshhBANhBRtIYQQooGQoi2EEEI0EFK07xO6yHCmfvAGkTEd6zuKg39kBDM+eIPG91EmUXMyfsIZQptE8sa6pfUdo4r7MVNYk0jeWp9c54/r9G/5uteGT59Mic1GaGQjFrz1bpW2mc/9loz0y2g0GjYuXsboWTNo0rr828aW/Pcjcq5m1GmWuMljKC0pQdcojK3vfFqlLfHpx9BfzsBFo+bA0rUAXD50zNHuGxZC64SelJWWEhbVio1/ebtOMnWtlGnLDZkGPP04BZev4qLRsH/pGgAuHTpeZZ2ZH79J/uWrZKWeY+/ilXWSaeDUCZTYbARHRrD83+9XaZvw7K/ITr+MWqNhy5IV9Bw1FBUqouJj2bwoGUNePlNeeJoTu1IIbhLJ8rffq5NMAL2njMVeUkJgo3A2/OejKm0jnnmK3EtXUGs07FyyimG/fZz8KxlEtm/LN//7FHO+nt5TxmIxmvCPCGPzxwvqJFNtx6/r5DFYTSb8wkP58ZPFdZJp/MPTKbHZiGgcyfv/eqtK2+wXnuNKejoajYYVCxfTvHUrYrvHo9aoOXH4KFcvXeYXT/+a44cOo/XxYdnnX9ZJpsSp47HbbAQ1imDVfz6o0jb2mdnkXCqfU9uWrKT7yCGoVCradotly+Jk0k+e5rnP3iUn/QqXU8/w/aK6OfD2nzoeu62EoMgIVt4wz8c9O5uc9PL5tHXJCnqMGooKaBsfy/eLlpN+MpUXPvsv2ZeucCn1DN8vXFYnmQAGTZtY/vxrFMGyf1d9/kx+9tdkVeyrzV8tB6D7kCT0ObkAtOjUnpjEvpgL9CiKwrcL66Zw3o+ZhkybSImthNDICBa//b8qbdN+/xsyK2rLt1+Vz5ceQwc5Ml3bvtBoIiginFUffnbXOR6oM22Nq4ZO8V35btlKCo0momKiHW3t4rpg0hv5btlKOveIR63RUGov4cS+g5w9fgpDXn6dZnHRaGjaLZpDKzZgNZlpFN3e0RYZ0xGr0cShFRtoFh+Di0aN/nLVFwzGzGxOfrcNXWQ4lw8fv/Hh7zpTs25dOLhiA1ZTIZHRHRxtjWM6YjGaOLhiA80rMhVcvlrtMQquZHL50HGyTp+tk0xqVw1R8bFsX76GIpOZVl06Odpax0ZTaDCyffka2vfoilqjZtfarykymXB1dyPzYjqm/HxsVitafx09Rw6pk0wAao2GVt1i2LN8HRaTmWZdrp+tNo/pRJHByJ7l62gdH4uLRs3OJavYs2I9ORfSadKpPRFtW9E8phMadzcyz5yrk0y1Hb/Qti1pHNMRjZsbOWkX6iSTxtWV2B7xrFmyDJPRSKfYGEdbdNc4jAY9a5YsI65XD9QaDbN+9UuKiorQuLpy7swZOsXGcDX9EhtXrCJpxHBc3VxrnUntqqFtt1h2LF9LkclMy0pzqlVsZwoNRnYsX0tU9/I5tWfdNxSZzLi6u5F18RIAuZczOHvoKJdS02qd51qmqPhYfli+hiKT6YZM5fP8h+VraNcjDrVGze61X1dkcifrYnp5pitXOXvoCJdPnamTTNdytesWy9bk1RSZTLTu0tnR1iY2GrPBwNbk1XSo2FdNo9qQfvoMiqIAkHP5Kqve/4RvFiwhtn/fBzaTxlVDh/iufJ+8ikKjiTYx1zNFxXbBrDfwffIqOvbohlqjoVlUG9JTz1BWVgZAs6g2RMV2wdXNjUtnajennF60TSYTK1eu5ODBgxiNRt566y3eeOMNrly5Uqf9DJ40jtmv/h9lSvlOMuQXEBwR5mgPDgvDkF8AgNViwUfnx5ZV6/l+5VoupZ1j5MNT6yxLl/HDGfanZ1AqBqwo34Bf+PXvFPcNC6Yo3wCAzWLF08/3po9TlK9ny78/psOwAbXOFDN+BCOqZNLjFx5SKVMIRfn6ikyWW2b69p//49Cqr+n5yCRcNOpaZUqYMIZHXv6DY2Kb8gsIDL8+ZgFhoZgqxqy4yIJW5wfAoa07OL5rL10S+tAyuhNZ6ZfZ8MmXfPiHV2uV55oe40cx4eXnUCrmkjlfj3+l8dOFhWB27Csr3n5+GLJzcVGr8Q8PI/XHFEJbNKW4sIiUlRuIHzscjZtbrTLVxfgFNW+CrbCIQ6u+JnrMUNS1LJBjpkzixT+/ilJWfrDU5+UTGhHhaA8JD0Nf8WLYWmTBT6ejbaeOpPzwI4f37mPqLx7lxy1bQQUjxo+lTCnDx/fm866m+k4YzYyXXnTsJ3NBAQGVxi4gLBRzQfmcslkseFfMqSNbd3Bi1146J/QGYOk/3ubHlesY/Og01LWc5/0mjOHhl1+krGI/mfP1BFbJdH0+FRdZHZkOb93BiV0pRFdk+mru2+xYsY4hj06vdSaA/hMf4hevzHEUO1O+nsCI67kCw0IxXctlsaLV6YhJ7MOhbT861jEV6CktsdOpdw82LVnxQGYaOHEsT7zyR8ecMhboCap0nAoMD8VYcC1TeW2JTezLgW07HOtEtGiGpbCQLSvW0H/saFxrcTxwetH+4IMPMBgM/Pjjj7z33nu0a9eOfv36sWzZrS/vbNq0iTlz5jBnzpwa9/Pt0hW89+pfcVGV/0p+Af7kXM10tOdkZuIX4A+Ah6cnJr2BRi2aAWDS6/EL9L+L3+7mDi3fwMa/vI3KpTyLV4AfhowsR7sxMwevgPInppunBxaDsdpjRMZ0xNXTAxTF8Ti1cXD5etZXyaTDkJFdKVM2XgG6ikyeN83kExpUngkoLSmpdSHalryaz1//By4VmXwC/MnLuD5m+ZlZ+FSMmbuXJ2a9gY69uwNgyMlFFxKEVudHkbE868CpE/C+xYuNO7F7+VqSX38TVcVc0gboKKg0fvrMbLSOfeVBocGAWqOh34wJfPP+fHyCAjDl5mMtLAKgpNiGq0ft9lVdjF9hXgHFFZnsNhuu7u61yrT6q6W88dKrqFxUAOgCA8i6ev3sPjsjE11gAAAeXp4Y9Hryc3MpNJsxGoz46vxQqWDjilWsX74So95AQS2veP2QvIYFf37DsZ+0/v7kVxq7/MwstP7lc8rN05NCvYEO1+ZUbh66kCD8Q4Nx8/QErs3z2u2n7cmr+fL1N3Cp2E/aAB15VTJdn0/uXh5VM+XkoQsJxj80GPeKTPaSElxrmQlgy7JVfPraXFSq8lw+ATryrl7PlZeZhc+1XJ4elJWW4u7lxcDJ4/APCab70CQA2sR0xtXNjXNHTzyQmTYvW8lHr/3dMad8/XXkVjpO5WVk4et/LZMnZaWleHp5MWjyeAJCQ+g5dBCGvHws5kIASmw23Dzufvycfk+7RYsWjBs3DoBPP/2UoUOHApCRcev7x0lJSSQlle/80Su+rnFf9hI7R1P2MWjiWLx9fTh18DC9hw6i2Gpl39Yf6JbYl0ETx3Jkdwqldjt9hg2ieVQbIpo15euKeyN1pcxu5+Lew3QZNxwPHy1XDp8galA/7NZi0n7YQ6u+3ekybjgXUg5SZi8loFkkYe1ao3FzI+tUGmpXDb2fmI45O5ezP+6ts0wX9h4iZtxwPHy8uXz4OO0GJWC3Wjnzwx5a9+1BzLjhnK/IFNissSNT5qk0PHy0dJvyEBknz5Bx4jS2IkutM5WW2DmVcoB+40fj5aMl7dBRug0ZgM1SzOHtPxKd0Jt+40dzYvc+Su2lxA5MQBcSTPMO7Vj7wWcUmUxMfuFpEiaMoaS4mMKbFKu7ymW3k7b3IN3Hj8TTR8uFQ8eIHpxIibWYE9t30T6hF93Hj+RMygHK7KXMmvdnSmw2Rj0bScaZ8/z41Uo6JyUQ/9Bwss5ewGI01zpTbcfvwr7DRA3sS/SYoeSevYjVVPtM9pISDuxOYfTkifj4+nL0wEEGDB+K1WJl55at9OqfyOjJE9m/czeldjvz3/0fM556ArXahZULv0Lr48Pzr7/Cgd0pfL1qteMMqzZKS+yk7j1An/Gj8PLRcvbQUeIGD8BmtXJ0+0469etNn/GjOLWnfE51GdAPv+AgmnVsx/oP5uPt50v/qeNJP5nKxeOpFBcV1UmmUykH6Dt+NF4+Ppw9dJSuQwZgs1g5sn0nnRN60Xf8aE7u3k+pvZSYgQnoQoJo1qFSpmkTSD+RysUTp7DWQaZruU7uPUDihDF4+fhw5tAR4ocMxGa1cmjbj8Qk9CFxwhiO79mHqUDPknnv0qlXd1AUzAYjLTq1Z+b/Pc+5oydImjqBN558+oHMZC+xczxlHwMmPIS3rw+nDx6hx5AkbFYrB7btIDaxDwMmPMSx3XsxFuhZOO8dOvfuUZHJwImU/XQf1J/EcaO4lHaOQqPprrOolLp4lvyE999/n5kzZ2I0Gvnggw+YM2cO7u7ufPzxxzz++OO33X50my7OjHdXunsE1neEakpx6jDelXP2wvqOcFP+LrU763UGf5fa38utaxtMl+s7QjWdvO6/516Zcw+hd82qlNZ3hAbBVnEb7H6z7NT+my53+pl2+/bteeqpp9DpdDzxxBP8/ve/x8XFhYSEBGd3LYQQQjxQnF60+/XrR9++fR33KP76179iNpuJjIx0dtdCCCHEA+We/J32tYINoNPp0Ol096JbIYQQ4oFSb3+nvXnz5vrqWgghhGiQnH6mvWDBzT8J6syZMwwcONDZ3QshhBAPDKefaaelpeHm5oa7u3uVfxrNA/cJqkIIIYRTOb1yzpw5k/T0dBITE6ss3759u7O7FkIIIR4o9+TDVVq0aFFteb9+/ZzdtRBCCPFAuadvRLv2GdNCCCGEuHNOP9M2Go188cUXHDhwgMLCQrRaLTExMcycORPfWn45gBBCCPFz4vSivWjRInr27MmsWbPw8vKiqKiI1NRUFi5cyOzZs53dvRBCCPHAcHrRDgsLIy4uzvGzVqslLi6OixcvOrtrIYQQ4oHi9KKdnZ3N7t27adOmDR4eHlitVk6fPk1ubq6zuxZCCCEeKE4v2tOmTWPx4sV8/vnnGI1G/Pz8iI2NZfr06c7uWgghhHigOL1oa7VakpKSGDFiBBEREQCcPn2affv2yTd9CSGEEHfA6UU7OTmZnTt3oigK0dHRzJo1i+DgYObPny9FWwghhLgDTi/aZrOZefPmAZCSksL8+fOZNWsW7u7uzu5aCCGEeKA4vWgHBgY6/h8fH4+3tzeLFi2q8fYulb7W835h4f77kJjthZn1HaGaMkWp7wg31crDr74jVHPaZqzvCNWEuHrVd4RqPFXq+o5QzYnigvqOcFPN3e+/z8G4H8evoXH6J6KdO3eOffv2OX7u0KEDUVFRnD171tldCyGEEA8Up59pT5kyBbPZXGVZbGwsr776qrO7FkIIIR4oTi/aoaGhhIaGVlt+sy8REUIIIcSt3dMvDBFCCCHE3ZOiLYQQQjQQUrSFEEKIBkKKthBCCNFASNEWQgghGggp2kIIIUQDIUVbCCGEaCCkaAshhBANhBRtIYQQooGQoi2EEEI0EFK0hRBCiAZCirYQQgjRQEjRFkIIIRqIn23RDmvSmHc2LK/vGA4BkRHM+uCfNI3pWN9RbuuPb/yNQWNG1XcM8YAYNOEhXv7o3fqOIWrh0b/+ie4jh9R3jJ8Fp381570ybNokSmwlhEZGsPDt/1Zpm/H735KZfhmNq4avFy8DoPfQQRTk5AKgCwpk0q+e4PThY3j7aFm/4Ks6ydR98kPYS2z4Nwpn0zufVGkb9PTj5F++ilqjIWXpGgDSDx1ztGsD/Ul8aiaXj57EQ+vN7sUr6yTT2BnTsNlshDduxMdv/rtK25PPP8uV9EtoNBpWL/qKZq1b0SW+G2qNmlNHjnH84CGatmqJj69vnWS5ZtzD07AV24hoHMmHb75dpe2pF37PlYvpaFw1rFr4Fc1bt6JL926o1RpOHjnK8YOHGPfwNEwGI6GNIljw3od1lmvg1AmU2GwER0aw/N/vV2mb8OyvyE6/jFqjYcuSFfQcNRQVKqLiY9m8KBl9Tg6jf/kYZ48cw8tHy6aFy+ok04jpkymx2QiLbMQXb1UtdI889zQZFeO3YfEyHpo1gyatWwKw+L8f4h8USHxiPwwFekBh7ZeL6yTT8IpMoZGNWHBDppnP/ZaM9MtoNBo2Ll7G6FkzaNK6/Gt5l/z3I/Iyszi8K4W+I4fWSZZr+k4Zh73ERkCjCNb/p+qcGPXMU+ReuoJao2HHklWM+O0T5F25SuP2UWz83yeoNRoeev43pO7eS1DjRqz7d93MqdEzpjrG7rN5/6nS9tjzz3C1YuzWLlrC+Edn0rRi7Ba8+z7hjSMZPWMqhSYTrTu05/kZv6DQZKqTXIlTx2O32QhqFMGq/3xQpW3sM7PJuVQ+z7ctWUn3kUNQqVS07RbLlsXJpJ88TXjLZnj5+tRJlmvux/GrTSaVyoWhv5zFhaMn8NR6s33R3Z8wPhBn2hpXDR27d2VT8koKTSbaxkQ72trFdcFsMLApeSWdenRDrdHQPKoNF1JPo5QpALTt0pmsS1fYunodvYcPRuPqWutMao2G5t2i2b9iA1aTmcbRHRxtTWM6YjGa2L9iAy3iY3DRqMm/fLXK9o2jO1Bw+SqH1n5LpyH9UddBJo2rK126d2P90mTMRhMdY7s42jrFxWI0GFi/NJnYnt1RazQ8PPtJLEVFuLq6cv7MGQD6DU7ih+821zpL5Uwx3eNZtzQZk9FIx9gYR1vnrrEY9XrWLU0mrmeP8ky/egpL4fVMrdpF0SkuFjd3N86nnqmzXGpXDVHxsWxfvoYik5lWXTo52lrHRlNoMLJ9+Rra9+iKWqNm19qvKTKZcHV3I/NiOi2jO5Fz+Qo712wkfuigOplTGlcNneO78u2ylZiNJtpVmuft42Iw6Q18u2wl0T3iUWs02O12ju87wNnjJzHk5ZN56Qpfvfcha79cRHz/hFrnuZapU3xXvlu2kkKjiagbnnsmvZHvlq2kc0WmUnsJJ/Yd5OzxUxjy8ikrKyP7ytWf6OHOqTUaWnWLYdfydVhMJpp3uX71qkVMZwoNRnYtX0fr+DhcNGp2LFnJ7hXryb6QTtNO7THlF2CzWtHqdHQdMbhOMmlcXYnu3o2NS5dTaDTRPqaLo61jXCwmvYGNS5fTpeK5Z7fbObr3AGnHT6LPy+fcqdP888X/463/e5Wdm76vs4KtdtXQtlssO5avpchkpmWled4qtnxf7Vi+lqju5fN8z7pvKDKZcXV3I+viJQBiBiZwcPP2OskD9+f41TZT8y4dyb18lb1rviZm6MBaHQ+cXrTtdjsbNmzgH//4B6+++irvvvsuR44cqbPHHzRxLL989f9QysoAMOYXEBwe5mgPCgvDmF8AQHGRBR+dH13792P/th2OdfZv+wFUMGDsKJQyBe9avmrsOn4Eo//0rONFQWG+Hl14iKPdLyyEwnw9ADaLFS+/6meup7fvBpWKmNFDUJQyPHy0tco0cvJEnnv9FRSlPJM+P5+QiAhHe0hEGPr8fACsFgt+Oh1tOnZg346dHN67n0mPPkK7zp04efgoCkqtslwzavJEnv/zK5RVjJ0+v4DQiPDrmcLD0VeMnaWoPFPbjh3Yu2Mnh/fuY/IvZtG0ZQuKzIWsX7aC4RPH4ubmVutcCRPG8MjLf3DkMuUXEFhpTgWEhWKqNKe0Oj8ADm3dwfFde+mS0IfD238ElYo+D41AUcpqfSYyZNI4fv3qnyirGD9jfgHBlfZVcFioY55bLRZ8dX58v2odm1euJT3tHKMenoqxoAB7iZ3YPr3Y+FXtz/wHTxrH7Ff/jzKlfD8Z8gsIjri+n4LDwjBUyuSj82PLqvV8v3Itl9LOMfLhqbXOcKOe40cx6eXnUSoymfP1+IeHOtp1YSGYHc89C95+fhiyc3FRqwkID+XUjyk069yB3PTLbPp0IV/Meb3WmYZPnsDvXn/JcYzS5xcQUnnswsMc89xaZMFXp2PTyjV8t3I1F9PO8tDM6ZgMBqwWCz0GJLJnS90UyL4TRjPjpRcducwFBQRU2lcBYaGYC8pz2SwWvCvm+ZGtOzixay+dE3rTrFN7zh89AXV0TLgfx68uMh3fthOVSkX8mGEoZWV4+t798dzpRXv+/Pnk5ubSpUsXgoODCQoK4vjx43z33Xe33GbTpk3MmTOHOXPm3Pbxv1u2kvdf/Ssql/JfxTfAn5yMTEd7bmYmvgH+ALh7eVJWWoaHlxdDpkwgIDSY3sMGASq2rlrH9yvXYjYYHAe/u7Vv+XrW/OUtVC4qALwDdOgzsh3thsxsvAN0ALh5elBkMFZ/EJWKQ2u/5eCabygymCgq0Ncq07oly3jz5ddQqcoz6QICyL56/Qwn+2omuoAAADw8PTHo9RTk5lFoNmMyGPHV+dGqfRQRTRoT26M7sT27E944slaZ1i5Zxr9eeg2XirHTBfiTdTXjeqaMDHQVY+fpVZ4p/1omoxFfPz/HzwC2YhvuHh61ygSwLXk1n7/+D0cunwB/8irNqfzMLHwqzSmz3kDH3t0BMOTkogsJQoWKH1dvYMeq9RQajI4if7e+WbqC/776F1wqxs83wJ+cSvsqJzPLMc89PD0x6g1EtmgGgFGvRxdYPrbtYrvg6ubK6SPHqK1vl67gvVf/iouqfD/5BfiTc/X6fsrJzMSvUiaT3kCjikwmvR6/QP9aZ7jRruVrWfr6v1BVZNIG6CjIyHK06zOz0Tqee54UGgyoNRoSZkzk6/fn4xMUgNZfR5Gx/Ey279RxN31RfSc2LEnm3y//2XGM0gX4k1157DIyHfPcw8sTo15PZIvmABgLro8dQNtOHTl97Hit8lzzQ/IaFvz5DUcurb8/+ZX2VX5mFlr/8lxunp4U6g10uDbPc/PQhQTROKo1IY0jaRsfR1T3rgRFRlTv6A7cj+NXF5lUKhUpazaSsnojRQajo8jfDaff0w4MDGTs2LEADBkyhI0bNzJs2DBWrVp1y22SkpJISkoC4KGV39y2D3uJnWN79pE0YSzePj6kHjxMr6FJFFus7N+2g7iEviRNGMvR3XsxFhTw5Zv/oUvvHqCASW/E20fLk6/8kWN79rF1zQbH2WhtlNrtnN97mLhxw/Hw0XLp8HE6DEqgxFrM6R9206ZvD+LGDedcykHK7KUENWtMeLvWaNzcyDiVhqunB6P+3+84v/cwh9d/VyeZ7CUlHNqzlxGTJqD19eHYgUMkDhuC1WJl99Zt9OyfwIhJEziwaw+ldjtf/Pd9pj75GGq1mtWLlnDx7Dn8/HW0i+6EUqZgNtb+Ep29pISDe1IYOWkCPr6+HDtwkP7DhmC1Wtm1ZRs9+ycyctIE9u/aTandzuf/fY9pTz2O2sWFVYu+Iv3ceRKGDGL4hHFcOJOGyXiTF0B3obTEzqmUA/QbPxovHy1ph47SbcgAbJZiDm//keiE3vQbP5oTu/dRai8ldmACupBgmndox9oPPsPTR8vMl17gVMoBdq79uo7Gz86RlH0MnjgWra8PJw8eps/QQRRbrezd+gPdEvsyeOJYDu9OodRup++wwTSPakOjZk3Z+FUybTp35Jd/+gOnjx5nxLTJvPz4r+ok09GUfQyaOBZvXx9OHTxM74pM+yoyDZo4liMVmfoMG0TzqDZENGvK11+V39frN2IoASHBdOgay/F9B2qdqdRuJ23vQXqOH4mnjw/nDx2jy+D+2KxWTmzfRYeEnvQcP5IzKfsps5fyi3l/psRmY9SzvyTjzDl2r1zPQ8//hp7jR2Evtt38RfUdspeUcHjPXoZNGo+3rw8nDh6i37DBFFus7Nm6ne79+zFs0ngOVTz3EoYPoWW7tkQ2a8r6xUsBaNa6FedPn651lspKS+yk7j1An/Gj8PLRcvbQUeIGD8BmtXJ0+0469etNn/GjOLWnfJ53GdAPv+AgmnVsx/oP5mPIyUWr86NZp/YoZWUUmcy1z3Qfjl9tMx385nsm/t9zpO09yL5139bqeKBS6uJo8hM+/fRTZs2ahYuLCyaTibVr1zJt2jQWLlzI9OnTb7v9Q21jbrvOvdbRXVffEarZXph5+5XusTLnTq271srDr74jVJNjt9Z3hGrUqOo7QjXN3Gp3m8gZTlhrdxXFWZq71+0bRuuCp0pd3xEajP8c23HT5U4/027bti2zZ8/G19eXwsJCXnzxRS5cuEBGRsbtNxZCCCGEg9OLdu/evenUqRM5OTk0atQIj4p7js8//7yzuxZCCCEeKPfk77R9fX3xreO/7RVCCCF+burt77Q3b667v/UVQgghfg6cfqa9YMGCmy4/c+YMAwcOdHb3QgghxAPD6WfaaWlpuLm54e7uXuWfRvPAfIKqEEIIcU84vXLOnDmT9PR0EhMTqyzfvr3uPvZOCCGE+DlwetFu0aIFLVq0qLa8X79+zu5aCCGEeKDc0zeiXfssZyGEEELcOaefaRuNRr744gsOHDhAYWEhWq2WmJgYZs6cKX8GJoQQQtwBpxftRYsW0bNnT2bNmoWXlxdFRUWkpqaycOFCZs+e7ezuhRBCiAeG04t2WFgYcXFxjp+1Wi1xcXFcvHjR2V0LIYQQDxSnF+3s7Gx2795NmzZt8PDwwGq1cvr0aXJzc53dtRBCCPFAcXrRnjZtGosXL+bzzz/HaDTi5+dHbGxsjb7hSwghhBDXOb1oa7VakpKSGDFiBBER5V+Qfvr0afbt20dCQoKzuxdCCCEeGE4v2snJyezcuRNFUYiOjmbWrFkEBwczf/58KdpCCCHEHXB60TabzcybNw+AlJQU5s+fz6xZs3B3d3d210IIIcQDxelFOzAw0PH/+Ph4vL29WbRoUY23j3X3d0asWskts9V3hGqMpfdfJgCti2t9RxBCiAeG04v2uXPn2LdvH127dgWgQ4cOFBcX8/XXXzu7a3EfWLrj+/qOIIQQDwynF+0pU6ZgNpurLIuNjeXVV191dtdCCCHEA8XpRTs0NJTQ0NBqy2/2JSJCCCGEuLV7+oUhQgghhLh7UrSFEEKIBkKKthBCCNFASNEWQgghGggp2kIIIUQDIUVbCCGEaCCkaAshhBANhBRtIYQQooGQoi2EEEI0EFK0hRBCiAZCirYQQgjRQEjRFkIIIRoIKdpCCCFEA/GzLNr+kRHM+OANGsd0rO8oQgghRI05/as575Wuk8dQWlKCrlEYW975tErbgKcfp+DyVVw0GvYvXQPApUPHq21vNZnwCw/lx08W10mmvlPGYS+xEdAogvX/+bBK26hnniL30hXUGg07lqxixG+fIO/KVRq3j2Lj/z5BrdHw0PO/IXX3XoIaN2Ldvz+8RS93ZvLDM7DZbDRq3Jh3//Vmlbbfvvg8ly+mo3HVsGzBIl75x99RFAU3Nzdys3N4e+4/mPzwDIwGA+GRjfj0f+/XSSYhhBA180CcabtoNDTr1oWDKzZgNRUSGd3B0dY4piMWo4mDKzbQPD4GF42agstXq2wf2rYljWM6onFzIyftQp1kUms0tOoWw67l67CYTDTvcv2svkVMZwoNRnYtX0fr+DhcNGp2LFnJ7hXryb6QTtNO7THlF2CzWtHqdHQdMbhOMmlcXenaozsrlyzFZDQSHRvraOvSNQ6j3sDKJUuJ79kTjUbD+2//h9fn/D92/bCDFV8toW37dnTpGoebuztpqafrJJMQQoiac/qZdllZGdu2bePAgQMYjUZ8fX2JjY0lISEBF5ebv2bYtGkTmzZtAsDrNo8fM34EkZ3aoZSVAVCUr8cvPITLh8vPpH3DQijK1wNgs1jw9POlMK+gymMENW+CrbCIQ6u+ZuK81zi7ax+ltpK7/p17jh9Fs+gOKEp5JnO+Hv/wUM4fOgaALiwEc6VM3n5+GLJzcVGrCQgPZcdXK2nWuQO56ZfZ9OlCzh06etdZrhk3ZTKdYrpQpigAFOTnE9YogsMHDgAQFhFBQX4+ABaLBT9/HVkZGQA0adaM9StXMWTUSAoLC1m9LJk33/8fu3fswFZsq3U2IYQQNeP0M+0vvviCS5cu0bdvXyZMmEDfvn25dOkSX3755S23SUpKYu7cucydO/e2j39w+XrW/+VtVBUvALwCdBgysh3txsxsvAJ0ALh5emIxGKs9RmFeAcWFRQDYbTZc3d3v5FesZtfytSx9/V+oVOWZtAE6CjKyHO36zOz/3969x0R1530cfzOOMDMOM6gUKLVAQZC2UQGVijfsxq1PvVS84ApWpE2aeolpTLQ3Nz6oMa2tvaZNniY2ogWxRgsltqkpNV7YioiglaKwgqAoSsDCyJ257B/WWW15Vlw9nmH4vv5iJsx8P5LxfOb85sw5GG/L1NrczACtlvgXE/nh/9Lx9h2CcbAPbZYbAExOmofBbLqvTN/s/prNf1+PxsMDgMFDhnD18r9XHK5eucLgIUMA0Ov1NP/WBED0uLGUnDgBQGNDA603WgDo6uzEy0t3X5mEEELcG8VL22w2k5KSQmxsLCNHjiQ2NpaUlBSMRuMDm2G3Wqk+cYroeTPQeQ+i9vSvPPnXeMInP8OlklL0Jm+i583gQmEJdquNoSGPE/BkOBFT4hio11FddBqtpyej5/wPDZU1dPxeTPfDZrVy/kQJcfNnoff25sKpUqKee5anpsRRVfILg8wm4ubP4p+FJ7FbbSx9738Z9mQ4s1cvY+Szkzj7j+MERgwnbv5srJ1dtPXwZuNeWbu7KSo4zty/LcTbZOJ0cTF/nfE8k5+dyqmik5h8zMz920IKjx3DarUCEDd5MgX5/wDgZMFxPL28mJM4n8p/nueG5f4zCSGE6D0Ph+P39VKFfPHFFyQlJWEy/XtPsbm5maysLJYtW3bXx28c9Rcl4/1XGuyutyR8pKVO7Qg9+v7no2pHEEKIPicwMLDH+xX/THv69Ols2LCB7u5u9Ho9bW1teHl5sWrVKqVHCyGEEG5F8dIOCQnhgw8+4Nq1azQ3N2M2m/H391d6rBBCCOF2Htr3tP39/Z1l3dLSQkdHB76+vg9rvBBCCNHnKX4gWlFREampqSxbtozCwkIALl68yKZNm5QeLYQQQrgVxfe0CwoK+OSTT7Db7eTm5tLW1sbUqVMZ8vvXi4QQQgjRO4rvaQcHB2M2mxk8eDBLly7lxo0bHDt2TOmxQgghhNtRvLQvXbrE9d/PtAUwe/Zs6urqqK2tVXq0EEII4VYUXx6Pi4ujuLiYadOmOe+bN28eer1e6dFCCCGEW1G8tKOjo50/2+125/nGn3/+eaVHCyGEEG5F8dK2WCzs3LmT4uJiWltbMRqNREdHk5KScsdZ0oQQQgjxnyle2rt27SIuLo7U1FQMBgNtbW2Ul5eTmZnJ8uXLlR4vhBBCuA3FSzsgIIAxY8Y4bxuNRsaMGUNNTY3So4UQQgi3onhp19fXU1BQQEREBDqdjo6ODioqKmhoaFB6tBBCCOFWFC/t5ORksrKy2LFjBxaLBbPZTExMDIsXL1Z6tBBCCOFWFC9to9HItGnTmDlzpvNSYxUVFRQVFREfH6/0eCGEEMJtKF7ae/fu5eeff8bhcDB69GhSU1N55JFHSE9Pl9IWQggh7oHipd3S0sKHH34IQGFhIenp6aSmpuLl5aX0aCGEEMK9OBSWm5t7x+3S0lJHZmamIy0tTenRf/Ljjz8+9Jl3I5l6xxUzORyumUsy9Y5k6j1XzNVfMyl+7vGqqiqKioqct59++mkiIyOprKxUevSf5OXlPfSZdyOZescVM4Fr5pJMvSOZes8Vc/XXTIovjy9atIiWlpY77ouJiSEtLU3p0UIIIYRbUby0/f398ff3/9P9oaGhSo8WQggh3Iriy+Ou5PYrjbkKydQ7rpgJXDOXZOodydR7rpirv2bycDgcDsWnCCGEEOK+9as9bSGEEKIvU/wzbVfQ1NTE7t27qamp4Z133lE7DgBXr15l9+7dhIaG0tjYiLe3NwsWLFA1k91uZ8uWLYSHh2O1Wrl69SorVqzA09NT1VwAXV1dvP3224waNYqUlBS147Bu3ToGDhwIgEajYf369SongitXrpCfn4+npydnz54lMTGR4cOHq5qpvr6eTZs2MXToUADa29sJCgpi5cqVqubKzc2lvr4ek8lEXV0dy5cvV/11vn//fq5fv45Op6O7u5vk5GQ8PDweeo6etpctLS1kZmbi7+9PXV0dSUlJ+Pj4qJrJbrfz008/8fXXX7N+/XqCgoIeWp7/L1N6ejpeXl7odDpqampITU194H+nflHa586dY+zYsVRXV6sdxamlpYWJEycybtw4AFavXk1MTIzqB+hFREQwf/58AN577z2OHz/O5MmTVc0EsHv3bkJCQtSO4TR69GgWLlyodgwnu93Ojh07eOONN9BoNMTHxzNgwAC1Y6HX63nllVcYNWoUAHv27HH+rJampiays7P58ssv0Wg0LvE6r66u5vDhw7z//vsAbN26lRMnThAbG/vQs/S0vdy1axcjR45kwoQJFBUV8dVXX7Fq1SpVM9XU1BAeHq7aibp6yqTT6Vi0aBEAOTk5fPPNN7z88ssPdG6/WB4fP348er1e7Rh3GD58uLOwARwOh+pnidNoNM7CttlsNDY2Os8Xr6YjR44wYsQI/Pz81I7idPHiRXJyctizZw/FxcVqx+H8+fMA/PDDD2RnZ3Py5Em8vb1VTgXe3t7Oku7u7qayspLIyEhVM3l6eqLVamlvbwego6ODxx9/XNVMdXV1ztUIuPmtmzNnzqiSpaftZUlJCREREQBERkY+9Nd8T5meeOIJVd/I95TpVmHDzW26Tqd74HP7RWm7usLCQkaPHs1jjz2mdhQATp06xbvvvsuYMWMICwtTNUttbS21tbU888wzqub4o4SEBBISEliwYAHZ2dmUlZWpmqehoYGKigqmTp3K3LlzKSsr4/Dhw6pm+qP8/HwmTpyodgwMBgNLlizh448/5vPPP2fIkCEEBASomiksLIzLly/T1dWFw+GgqqrK+abCFTQ3NzsLSq/X09rais1mUzmV62ptbeWXX37hhRdeeODPLaWtstLSUkpLS1m6dKnaUZyioqJYt24d9fX1HDhwQNUshYWFeHp6kpOTQ3l5OZWVlXz33XeqZgKcnxVrNBoiIyP59ddfVc2j1+sJDAzEYDAAuESmPyooKGDChAlqx6C6uprc3FzefPNNVq5ciclkYu/evapm8vPz49VXX2Xfvn18//33DBs2DF9fX1Uz3c5sNjvfRLS3tzNo0CCX+PjFFbW1tbFt2zaWL1+O0Wh84M8vpa2i4uJiTp8+zUsvvURTUxMVFRWq5qmtrb1j2cvPz49r166pmAjmzZvHggULSEhIYMSIEYSFhTFz5kxVM12+fJmDBw86b9fV1am+pxYeHk5LSwt2ux24uef96KOPqprpdqWlpURERKDVqn8YzfXr1zEajc7S8fHxoaurS+VUNy9jnJSUxMyZM2ltbWXSpElqR3KKjo52bp/OnTtHTEyMyolck8ViYdu2bSxZsgQ/Pz8KCgoe+Az1/wc9BGVlZRw5coTffvuNffv2MXv2bNWPFK2qquKjjz4iLCyMDRs20NnZyfTp052fG6lBq9Vy8OBBLly4gM1mo7a29oEfRPHfKigo4OzZs1itVvLz81XdoOn1ek6ePMn169dpb2/H19dX9WVfo9HI4sWLSU9Px2QyYbFYVP82wu3y8vJc5rUUFRVFcXExO3fuxGAwcOnSJVJTU9WOxfbt24mMjGTgwIHExsYybNgwVXL0tL1MTk4mIyODuro6rl27xpIlS1TP1NXVxYEDB2hrayMvL49JkyY91O1nT5k2b96MzWbj008/BW4emDZ+/PgHOldOriKEEEL0EbI8LoQQQvQRUtpCCCFEHyGlLYQQQvQRUtpCCCFEHyGlLYQQQvQR/eIrX0IIZRUVFZGRkYGPjw9paWlqxxHCbcmethDinh06dOiOch47diwJCQmq5RGiv5DSFkIIIfoIWR4Xwo3k5eWRnZ1NeHg4BoOB8vJygoODSUxMJCsriwsXLjBr1iymT5+O1WolKyuL8vJyAEaMGEFSUhJarZatW7dSUlJCYmIiFRUVXLp0yfm4srIycnJyaGpqIi0tjaCgIOfZzhwOBxkZGZw9exaNRsOaNWswm81q/kmEcCuypy2EG5k2bRrx8fGUl5fz4osvsmXLFs6cOcO3337L6tWrWb16NZmZmdhsNnJycqiurmbjxo1s3LjReblRgDVr1uDj40NDQwOvv/46r732GhkZGdhsNp566ikSEhIICQkhLS3tjtOTVlVV8dxzz7F582YMBsMd52gXQtw/KW0h3FBYWBgGgwGtVktAQADBwcF4eHgQHBxMR0cHzc3NHDlyhClTpqDRaNBoNEyZMoVDhw7d8TxRUVEABAUF0dnZSXNz83+cGxgY6LzueXBwMPX19Ur884Tot6S0hXBDt659DDBgwADn7VtXtrJarTQ2NmIymZy/ZzKZaGxs7PF5bl1gx2q19nquVqu96+8LIe6NlLYQ/ZSvry8Wi8V522KxMHToUBUTCSHuRkpbiH4qPj6eo0ePYrfbsdvtHD16lKlTp/bqsTqdjs7OTgC2bt2KzWZTMKkQ4ha5NKcQbiQ/P5+srCy6urpITEzEYrGwf/9+fHx8WLFiBbm5uRQWFhIeHs7atWvZv38/586dAyAiIoLk5GS0Wi2fffYZx44dIzAwkLVr17Jz507n49566y0ANm3ahKenJ6GhoYwbN45t27bR1NTEjBkzCA0NZfv27XR1dTFnzhxmzZql5p9FCLchpS2EEEL0EbI8LoQQQvQRUtpCCCFEHyGlLYQQQvQRUtpCCCFEHyGlLYQQQvQRUtpCCCFEHyGlLYQQQvQRUtpCCCFEHyGlLYQQQvQR/wKOr99kaIn8fwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_monthly_ic = al.performance.mean_information_coefficient(\n",
    "            M_factor_data,\n",
    "            by_time=\"M\",\n",
    "        )\n",
    "\n",
    "mean_monthly_ic.index = pd.MultiIndex.from_tuples(\n",
    "        [[i.year, i.month] for i in mean_monthly_ic.index],\n",
    "        names=[\"year\", \"month\"])\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.title(\"Monthly Mean 1 Period IC\")\n",
    "\n",
    "# 1 periods IC headmap\n",
    "sns.heatmap(mean_monthly_ic['1D'].unstack(),\n",
    "            annot=True,\n",
    "           annot_kws={\"size\": 7},\n",
    "           linecolor='white',\n",
    "           center=-0.1,\n",
    "           cbar=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-10T15:39:38.688086Z",
     "start_time": "2020-09-10T15:39:36.407988Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factor_data = M_factor_data[['1D', 'factor_quantile']].copy()\n",
    "\n",
    "pivot_ret = pd.pivot_table(factor_data.reset_index(\n",
    "), index='date', columns='factor_quantile', values='1D')\n",
    "\n",
    "# 多空收益\n",
    "ls_ret = pivot_ret[1] - pivot_ret[5]\n",
    "\n",
    "# 计算累计收益\n",
    "mpl.rcParams['font.family'] = 'Microsoft YaHei'\n",
    "algriothm_cum = np.exp(np.log1p(pivot_ret).cumsum())\n",
    "algriothm_cum['long_short'] = np.exp(np.log1p(ls_ret).cumsum())\n",
    "algriothm_cum.plot(figsize=(18, 8), title='W反转因子');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 反转之力的微观来源\n",
    "\n",
    "为了更好地展开讨论，我们首先引入一张逐笔成交金额的金字塔图。我们通过对 2013-2020年间全部A股逐笔成交明细数据的统计，画出了A股逐笔成交金额的整体分布，其中横向红色柱子的长度代表该金额水平上的成交笔数。 从形状上看，这是一个畸形的金字塔，底部极宽，顶部极窄。其表征的义是，大多数被撮合成功的成交，其成交金额都是很小的，中位数在13616.03元，18969.78元是 80% 分位，22948.54元已经是 90%分位，10万元以上的成交几乎是凤毛麟角。最显眼的红点是金字塔的重心（即平均值），意义是全部A股在此6年间的平均每笔成交金额为14869.51元。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-13T12:57:27.271331Z",
     "start_time": "2020-09-13T12:56:42.776194Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "成交额均值:14869.5179\n",
      "中位数:13616.0327\n",
      "80%分位:18969.7874\n",
      "90%分位:22948.5487\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.concat([amt_df.stack(), dealAmount.stack()], axis=1)\n",
    "df.columns = ['amt', 'dealamount']\n",
    "\n",
    "# 丢弃缺失值\n",
    "df_ = df.dropna().copy()\n",
    "\n",
    "# 统计平均成交\n",
    "df_['rng'] = pd.cut(df_['amt'], 150)\n",
    "stat_df = df_.reset_index(drop=True)\n",
    "\n",
    "# 按金额统计笔数分布\n",
    "stat_ser = stat_df.groupby('rng')['dealamount'].apply(\n",
    "    lambda x: np.ptp(x) if len(x) > 0 else np.nan)\n",
    "stat_ser = stat_ser.dropna().copy()\n",
    "\n",
    "# 统计平均成交\n",
    "describe_ser = df_['amt'].describe(percentiles=[0.8, 0.9])\n",
    "\n",
    "mean = describe_ser['mean']\n",
    "median = describe_ser['50%']\n",
    "percent_80 = describe_ser['80%']\n",
    "percent_90 = describe_ser['90%']\n",
    "\n",
    "print('成交额均值:%.4f\\n中位数:%.4f\\n80%%分位:%.4f\\n90%%分位:%.4f\\n' %\n",
    "      (mean, median, percent_80, percent_90))\n",
    "\n",
    "# 画图所需\n",
    "idx_y = range(len(stat_ser))\n",
    "v = stat_ser.values\n",
    "\n",
    "cat = np.array(stat_ser.index.values)\n",
    "right_label = [i.right for i in cat]\n",
    "\n",
    "mpl.rcParams['font.family'] = 'Microsoft YaHei'\n",
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "plt.title('逐笔成交金额的金字塔')\n",
    "\n",
    "plt.barh(y=idx_y,\n",
    "         width=- v / 2,\n",
    "         tick_label=right_label,\n",
    "         color='#F08080')\n",
    "\n",
    "plt.barh(y=idx_y,\n",
    "         width=v / 2,\n",
    "         tick_label=right_label,\n",
    "         color='#F08080')\n",
    "\n",
    "plt.axvline(0, ls='--', color='black', alpha=0.5)\n",
    "plt.xticks([])\n",
    "y_major_locator = MultipleLocator(5)  # 每隔5个值显示一个标签\n",
    "ax = plt.gca()\n",
    "ax.yaxis.set_major_locator(y_major_locator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "逐笔成交金额的金字塔，不是本文讨论的重点，但它却为我们提供了一个理解 W式切割的绝佳视角。不妨设想，对于每一个股票、每一个交易日，我们都分别画 一个类似的金字塔（形态会略显毛糙），W式切割等于是说：让我们回看过去 20 个 交易日，然后比较每日金字塔的重心（平均值）高低，取重心高的 10 日的涨跌幅加总得到M_high，重心低的 10 日则得到M_low。在这个图象之下，如果我们进一步追问，重心代表了交易行为的什么特征呢？答案是相当模糊的，原因就在于，平均值是一个很弱的统计量，重心（平均单笔成交金额）提高，有可能是因为大单成交增加了，有可能是因为小单成交减少了，也有可能是大单被更大的大单替代了。换 言之，从均值出发，很难反推关于逐笔成交金额原始分布的细节信息。顺着这一思路，为使W式切割能够得到更多的微观信息，我们考虑用分位数代替平均值，作为新的切割标准，如下表："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|因子构造|\n",
    "|--|\n",
    "|步骤1:对选取股票S,回溯取其20日的数据;|\n",
    "|步骤2:计算股票S每日的**平均单笔成交金额**(成交金额/成交笔数)分布的**1/16分位值**;|\n",
    "|步骤3:单笔成交金额高的10个交易日,涨跌幅加总,记作M_high;|\n",
    "|步骤4:单笔成交金额低的10个交易日,涨跌幅加总,记作M_low;|\n",
    "|步骤5:理想反转因子M=M_high-M_low;|\n",
    "|步骤6:对所有股票进行以上操作计算各自的**理想反转因子M**|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-13T06:11:33.850605Z",
     "start_time": "2020-09-13T06:11:33.840656Z"
    }
   },
   "outputs": [],
   "source": [
    "def cala_hl_factor(df1: pd.DataFrame, df2: pd.DataFrame, win_size: int, q: float) -> pd.Series:\n",
    "    '''\n",
    "    df1,df2:数据需要对齐 以df1为基准对齐\n",
    "    df1:amt\n",
    "    df2:pct_chg\n",
    "    '''\n",
    "\n",
    "    # 数据对齐\n",
    "    df1, df2 = df1.align(df2, join='right')\n",
    "    \n",
    "    idx = df1.index[win_size - 1:]\n",
    "    # rolling\n",
    "    iidx = np.arange(len(df1))\n",
    "    shape = (iidx.size - win_size + 1, win_size)\n",
    "    strides = (iidx.strides[0], iidx.strides[0])\n",
    "    res = np.lib.stride_tricks.as_strided(\n",
    "        iidx, shape=shape, strides=strides, writeable=True)\n",
    "\n",
    "    # 获取条件\n",
    "    def _get_cond(df1: pd.DataFrame, res: list) -> pd.Series:\n",
    "        \n",
    "        slice_df = df1.iloc[res]\n",
    "        rank_df = slice_df.quantile(q)\n",
    "        tmp_name = slice_df.index[-1]\n",
    "        cond = (slice_df >= rank_df)\n",
    "    \n",
    "        return cond\n",
    "    \n",
    "    \n",
    "    # 获取cond，和拆分后的列\n",
    "    x_list = [[_get_cond(df1,i),df2.iloc[i]] for i in res]\n",
    "    \n",
    "    m_high = pd.concat([np.sum(i[0]*i[1]) for i in x_list],axis=1).T\n",
    "    m_high.index = idx\n",
    "    m_low = pd.concat([np.sum(~i[0]*i[1]) for i in x_list],axis=1).T\n",
    "    m_low.index = idx\n",
    "        \n",
    "    return m_high,m_low\n",
    "\n",
    "def grid_w_factor(amt_df: pd.DataFrame, ret_df: pd.DataFrame) -> dict:\n",
    "    '''\n",
    "    获取1/16至15/16分位数的m_high，m_low\n",
    "    '''\n",
    "\n",
    "    df_dict = {}\n",
    "\n",
    "    step = np.arange(1, 16)/16\n",
    "    label = [f'{i}/16' for i in range(1, 16)]\n",
    "\n",
    "    for n, i in tqdm(zip(label, step), desc='计算中', total=len(step)):\n",
    "\n",
    "        df_dict[n + '_h'],df_dict[n + '_l'] = cala_hl_factor(amt_df, ret_df, 20, i)\n",
    "\n",
    "    return df_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-13T07:19:41.901429Z",
     "start_time": "2020-09-13T06:12:05.662058Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "计算中: 100%|███████████████████████████████████████████████████████████████████████| 15/15 [1:07:36<00:00, 270.42s/it]\n"
     ]
    }
   ],
   "source": [
    "# 各分位数切割W因子\n",
    "factor_dict = grid_w_factor(amt_df,ret_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-13T10:25:06.971839Z",
     "start_time": "2020-09-13T10:24:54.396454Z"
    }
   },
   "outputs": [],
   "source": [
    "# 数据储存\n",
    "# with open('m_dict1.pkl','wb') as pkl_file:\n",
    "#     pickle.dump(factor_dict,pkl_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T02:05:14.458697Z",
     "start_time": "2020-09-14T02:04:53.915565Z"
    }
   },
   "outputs": [],
   "source": [
    "# 数据读取\n",
    "with open('m_dict1.pkl','rb') as pkl_file:\n",
    "    factor_data = pickle.load(pkl_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T05:10:53.255556Z",
     "start_time": "2020-09-14T05:10:53.246553Z"
    }
   },
   "outputs": [],
   "source": [
    "def grid_cala_ic(factor_df: dict, close_df: pd.DataFrame, method: str = 'normal') -> pd.DataFrame:\n",
    "\n",
    "    ic_dic = {}\n",
    "    for k, v in factor_df.items():\n",
    "\n",
    "        x_factor = al.utils.get_clean_factor_and_forward_returns(v.stack(),\n",
    "                                                                 close_df.shift(\n",
    "            -1),\n",
    "            quantiles=1,\n",
    "            periods=(1,))\n",
    "\n",
    "        ic_dic[k] = cala_rank_ic(x_factor, method)\n",
    "\n",
    "    return ic_dic\n",
    "\n",
    "\n",
    "def cala_rank_ic(factor_df: pd.DataFrame, method: str) -> float:\n",
    "\n",
    "    if method == 'rank':\n",
    "\n",
    "        return st.spearmanr(factor_df['1D'], factor_df['factor'])[0]\n",
    "\n",
    "    else:\n",
    "\n",
    "        return st.pearsonr(factor_df['1D'], factor_df['factor'])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T05:22:33.394901Z",
     "start_time": "2020-09-14T05:17:48.995876Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    }
   ],
   "source": [
    "# 获取各分位数W因子IC值\n",
    "ic_dict = grid_cala_ic(factor_data,close_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们的计算结果显示，当采用1/16分位值作为切割标准时，M_high的IC为0.0012， M_low 的IC为0.034，M_high成分都呈现了反转特性。考虑到“切割”的原始动机是将动量与反转两个成分尽可能地拆分开，显然，这不是一个很理想的切割结果。\n",
    "\n",
    "接下来依此类推，我们可以改用其他分位值作为切割标准，分别测试2/16分位、3/16分位、4/16分位…15/16分位的情形。这里得出了非常有趣的结论：随着分位值的提高，M_high 的反转特性越来越强（蓝点，IC是负的，但绝对值越来越大）,M_low的反转特性逐渐提高（红点，IC 从正值慢慢向零靠拢）。总体效果像一个向右敞开的喇叭口，随着越往横轴右边（高分位），M_high 与 M_low之间的 IC 差距越大。也就是说，切割标准选用越高的分位值，W式切割的效果越好。这意味着，决定反转强度的因素，主要来源于成交金额分布的高分位区，也即大单成交区。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T05:22:37.611631Z",
     "start_time": "2020-09-14T05:22:37.481979Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x226eff9a188>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ic_df = pd.Series(ic_dict)\n",
    "ic_df = ic_df.to_frame('ic')\n",
    "\n",
    "# 整理数据格式\n",
    "ic_df['attr'] = list(map(lambda x: 'M_'+x[-1].upper(), ic_df.index))\n",
    "ic_df['label'] = list(map(lambda x: int(x.split('/')[0]), ic_df.index))\n",
    "\n",
    "ic_df = pd.pivot_table(ic_df,\n",
    "                       index='label',\n",
    "                       columns='attr',\n",
    "                       values='ic')\n",
    "\n",
    "ic_df.plot(marker='s')\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T02:14:23.406909Z",
     "start_time": "2020-09-14T02:14:23.401922Z"
    }
   },
   "outputs": [],
   "source": [
    "def grid_factor(factor_data:dict)->dict:\n",
    "    \n",
    "    k = [f'{i}/16' for i in range(1,16)]\n",
    "    \n",
    "    w_dict = {}\n",
    "    \n",
    "    for i in k:\n",
    "        \n",
    "        w_dict[i] = factor_data[i+'_h'] - factor_data[i+'_l']\n",
    "        \n",
    "    return w_dict\n",
    "\n",
    "def cal_w_icir(w_factor:dict,close_df:pd.DataFrame)->pd.DataFrame:\n",
    "    \n",
    "    df = pd.DataFrame(index=list(w_factor.keys()),columns=['IC','IR'])\n",
    "    \n",
    "    for k,v in w_factor.items():\n",
    "        \n",
    "        x_factor_data = al.utils.get_clean_factor_and_forward_returns(v.stack(),\n",
    "                                                                      close_df.shift(-1),\n",
    "                                                                      quantiles=1,\n",
    "                                                                      periods=(1,))\n",
    "\n",
    "        rank_ic_ser = al.performance.factor_information_coefficient(x_factor_data)\n",
    "        ic_mean = rank_ic_ser['1D'].mean()\n",
    "        ir = ic_mean / rank_ic_ser['1D'].std()\n",
    "        \n",
    "        df.loc[k,'IC'] = ic_mean\n",
    "        df.loc[k,'IR'] = ir\n",
    "        \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T04:36:22.746627Z",
     "start_time": "2020-09-14T04:32:01.439075Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:4196: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(SpearmanRConstantInputWarning())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:4196: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(SpearmanRConstantInputWarning())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:4196: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(SpearmanRConstantInputWarning())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:4196: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(SpearmanRConstantInputWarning())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:4196: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(SpearmanRConstantInputWarning())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:4196: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(SpearmanRConstantInputWarning())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:4196: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(SpearmanRConstantInputWarning())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:4196: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(SpearmanRConstantInputWarning())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:4196: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(SpearmanRConstantInputWarning())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:4196: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(SpearmanRConstantInputWarning())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:4196: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(SpearmanRConstantInputWarning())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:4196: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(SpearmanRConstantInputWarning())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:4196: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(SpearmanRConstantInputWarning())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:4196: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(SpearmanRConstantInputWarning())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 18.9% entries from factor data: 18.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:4196: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(SpearmanRConstantInputWarning())\n"
     ]
    }
   ],
   "source": [
    "w_factor = grid_factor(factor_data)\n",
    "\n",
    "icir = cal_w_icir(w_factor,close_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T04:45:48.585587Z",
     "start_time": "2020-09-14T04:45:48.384098Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:1235: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(xticklabels)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:label='b1f1bb99-391a-4df4-9f29-9c3b6884b1df'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart = icir.plot.line(y='IC',marker='s',title='不同分位下的W式切割')\n",
    "icir.plot.line(y='IR',secondary_y=True,marker='s',ax=chart)"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3.7.4 64-bit ('base': conda)",
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